From 99c80490f1bc9cded7d0147803c6a9b34270e4bb Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 4 Nov 2022 09:34:47 -0300
Subject: [PATCH 01/89] depend forcats

---
 DESCRIPTION             |   4 +++-
 R/kin.R                 |   1 +
 data/swe_surv.rda       | Bin 81904 -> 0 bytes
 man/kin.Rd              |   2 ++
 vignettes/Reference.Rmd |  10 ++++++----
 5 files changed, 12 insertions(+), 5 deletions(-)
 delete mode 100644 data/swe_surv.rda

diff --git a/DESCRIPTION b/DESCRIPTION
index f14db16..9b3e9db 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -14,12 +14,14 @@ RoxygenNote: 7.2.1
 Suggests: 
     knitr,
     rmarkdown,
-    testthat (>= 3.0.0)
+    testthat (>= 3.0.0),
+    ggplot2
 VignetteBuilder: knitr
 Imports: 
     dplyr,
     tidyr,
     purrr,
+    forcats,
     HMDHFDplus,
     progress,
     matrixcalc,
diff --git a/R/kin.R b/R/kin.R
index e9a5669..b585345 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -11,6 +11,7 @@
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
 #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
+#' @param stable logic. Deprecated. Use `time_invariant`.
 #' @return A list with:
 #' \itemize{
 #'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age.}
diff --git a/data/swe_surv.rda b/data/swe_surv.rda
deleted file mode 100644
index f00af74cc482396fab5d01db97089bdb919011d3..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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zkw6mIED>|Bx|szNXW~$2Uq04OTbhMHHzfi9<pTW&9gLOAch{}1MiXr&!<0)YK!e^w
zC9`=&$o||JwBy6h-B6%_Noha;wR&&e$h!`%KB3IJh#o0Kxs;ZWiHad9vdF#C)MeTV
zRqv0$(La|j^LC}jLZCzzDURbuRN+oA!E6v(10>Kp7fU+(LjBh|9M1PI;bo(+LM)H=
zMZTgD+YrZ%zzLvu0?01_9soQA0=?gz%FHCTm0flw1RzWAwwRT;9tH0MKcWv+qV4X$
zT;Sy3B3M7koae(pp-*jc?ihgp0p<Boe~6r1R)TBE%R)}AeLjSoWC%b#e<{67b}yXE
z?@Q-n>tmqnlNCS%*oVp4m{MfzZI>90eYV9wkO^4P4B0SaGUxC0QoTLfIRxIAB0~@1
pK?gw2t4rfwHa@N@-WtobZ(M0c8(5$lg*X4j+>uTcBsB0m#{l+9j8p&s

diff --git a/man/kin.Rd b/man/kin.Rd
index 21c8115..bccb33e 100644
--- a/man/kin.Rd
+++ b/man/kin.Rd
@@ -35,6 +35,8 @@ kin(
 \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
 
 \item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring, this needs to be set as 1.}
+
+\item{stable}{logic. Deprecated. Use \code{time_invariant}.}
 }
 \value{
 A list with:
diff --git a/vignettes/Reference.Rmd b/vignettes/Reference.Rmd
index 388a072..33560b2 100644
--- a/vignettes/Reference.Rmd
+++ b/vignettes/Reference.Rmd
@@ -11,7 +11,7 @@ vignette: >
 ---
 
 ```{r, include=FALSE}
-devtools::load_all()
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 ```
 
 In this vignette, we'll demonstrate how `DemoKin` can be used to compute kinship networks for an average member of a given (female) population. Let us call her Focal: an average Swedish woman who has always lived in Sweden and whose family has never left the country.
@@ -25,7 +25,9 @@ In order to implement the time-invariant models, the function `DemoKin::kin` exp
 
 ```{r, message=FALSE, warning=FALSE}
 library(DemoKin)
-library(tidyverse)
+library(tidyr)
+library(dplyr)
+library(ggplot2)
 library(knitr)
 # First, get vectors for a given year
 swe_surv_2015 <- swe_px[,"2015"]
@@ -245,7 +247,7 @@ demokin_svk1980_caswell2020 %>%
   filter(kin %in% c("oa","ya"), age_focal %in% c(20,60)) %>% 
   mutate(parity = as.integer(stage_kin)-1,
          parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)),
-         parity = fct_rev(parity)) %>% 
+         parity = forcats::fct_rev(parity)) %>% 
   group_by(age_focal, age_kin, parity) %>% 
   summarise(count= sum(living)) %>% 
   ggplot() +
@@ -263,7 +265,7 @@ demokin_svk1980_caswell2020 %>%
   filter(kin %in% c("d","m")) %>% 
   mutate(parity = as.integer(stage_kin)-1,
          parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)),
-         parity = fct_rev(parity)) %>% 
+         parity = forcats::fct_rev(parity)) %>% 
   group_by(age_focal, kin, parity) %>% 
   summarise(count= sum(living)) %>% 
   DemoKin::rename_kin() %>% 

From 76df24339c026e783594a366b08a4d239ec41fd1 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Sat, 19 Nov 2022 10:46:24 -0300
Subject: [PATCH 02/89] fix no pi or N case

---
 R/kin_time_variant.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index bc962fc..a48306b 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -42,8 +42,10 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL,
     if(is.null(N)){
       # create pi and fill it during the loop
       message("Stable assumption was made for calculating pi on each year because no input data.")
+      pi_N_null_flag <- TRUE
       pi <- matrix(0, nrow=ages, ncol=n_years_data)
     }else{
+      pi_N_null_flag <-FALSE
       pi <- rbind(t(t(N * f)/colSums(N * f)), matrix(0,ages,length(years_data)))
     }
   }
@@ -72,7 +74,7 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL,
     # print(iyear)
     Ut <- as.matrix(U[[iyear]])
     ft <- as.matrix(f[[iyear]])
-    if(is.null(pi)){
+    if(pi_N_null_flag){
       A <- Ut[1:ages,1:ages] + ft[1:ages,1:ages]
       A_decomp = eigen(A)
       w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))

From f9bb9e51e3cefad0e7bda3b33f0eefeb4e86f6f7 Mon Sep 17 00:00:00 2001
From: alburezg <gatemonte@gmail.com>
Date: Wed, 4 Jan 2023 18:20:03 +0100
Subject: [PATCH 03/89] Added labels to plot_diagram

---
 R/plot_diagramm.R | 157 +++++++++++++++++++++-------------------------
 1 file changed, 71 insertions(+), 86 deletions(-)

diff --git a/R/plot_diagramm.R b/R/plot_diagramm.R
index 2497186..0305e38 100644
--- a/R/plot_diagramm.R
+++ b/R/plot_diagramm.R
@@ -1,90 +1,75 @@
 #' plot a Kin diagram (network)
 
-#' @description Given estimation of kin counts from `kins` function, draw a network diagramm.
-#' @param kin_total data.frame. With columns `kin` with type and `count` with some measeure.
-#' @param rounding numeric. Estimation could have a lot of decimals. Rounding will make looks more clear the diagramm.
-#' @return A plot
+#' @description Draws a Keyfitz-style kinship diagram given a kinship object created by the `kin` function. Displays expected kin counts for a Focal aged 'a'.
+#' @param kin_total data.frame. values in column `kin` define the relative type - see `demokin_codes()`. Values in column `count` are the expected number of relatives.
+#' @param rounding numeric. Number of decimals to show in diagram.
+#' @return A Keyfitz-style kinship plot.
 #' @export
 
-plot_diagram <- function(kin_total, rounding = 3){
-
-  vertices <- data.frame(
-    nodes =  c("ggd", "gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys")
-    , x = c(1, 1, 1, 1, 1, 1, 1, 0, -1, 0, -1, 2, 3, 2, 3)
-    , y = c(0, 1,   2, 3,   4, 5,   6,  4, 3,   3,   2, 4, 3,   3,   2)
-  )
-
-  d <- data.frame(
-    from = c("ggd", "gd", "d", "Focal", "m", "gm", "gm", "oa", "m", "os", "gm", "ya", "m", "ys")
-    , to = c("gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys")
-  )
-
-  # Add values
-  lookup <- c(with(kin_total, paste0(kin, " \n", round(count, rounding))), "Focal")
-  names(lookup) <- c(kin_total$kin, "Focal")
-
-  vertices$nodes <- lookup[vertices$nodes]
-  d$from <- lookup[d$from]
-  d$to <- lookup[d$to]
-
-  # Plot
-
-  b <- igraph::graph_from_data_frame(vertices = vertices, d= d, directed = FALSE)
-
-  plot(
-    b
-    , vertex.size = 30
-    , curved = 1
-    , vertex.color = "#FFF1E2"
-    , vertex.shape = "circle"
-    , vertex.label.cex = 0.8
-    , vertex.label.color = "black"
-    , label.degree = -pi/2
-    , edge.width = 2
-    , edge.color = "black"
-  )
-
-}
-
-# old function
-
-# plot_diagram <- function(kin_total, rounding = 3){
-#   # https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html
-#   # https://color.hailpixel.com/#D9E9BE,BF62CB,94C2DB,79D297,CDA76A,C8695B
-#
-#   kin_total <- kin_total %>% mutate(count = round(count,digits = rounding))
-#
-#   DiagrammeR::mermaid(
-#     paste0("graph TD
-#
-#   GGM(ggm: <br>",                       kin_total$count[kin_total$kin=="ggm"]  ,")
-#   GGM ==> GM(gm: <br>",                 kin_total$count[kin_total$kin=="gm"]  ,")
-#   GM  --> AOM(oa: <br>",                kin_total$count[kin_total$kin=="oa"]  ,")
-#   GM  ==> M(m: <br>",                   kin_total$count[kin_total$kin=="m"]  ,")
-#   GM  --> AYM(ya: <br>",                kin_total$count[kin_total$kin=="ya"]  ,")
-#   AOM  --> CAOM(coa: <br>",             kin_total$count[kin_total$kin=="coa"]  ,")
-#   M   --> OS(os: <br>",                 kin_total$count[kin_total$kin=="os"]  ,")
-#   M   ==> E((Ego))
-#   M   --> YS(ys: <br>",                 kin_total$count[kin_total$kin=="ys"]  ,")
-#   AYM  --> CAYM(cya: <br>",             kin_total$count[kin_total$kin=="cya"]  ,")
-#   OS   --> NOS(nos: <br>",              kin_total$count[kin_total$kin=="nos"] ,")
-#   E   ==> D(d: <br>",                   kin_total$count[kin_total$kin=="d"]  ,")
-#   YS   --> NYS(nys: <br>",              kin_total$count[kin_total$kin=="nys"]  ,")
-#   D   ==> GD(gd: <br>",                 kin_total$count[kin_total$kin=="gd"]  ,")
-#   style GGM fill:#a1f590, stroke:#333, stroke-width:2px;
-#   style GM  fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center;
-#   style M   fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center
-#   style D   fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center
-#   style YS  fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center
-#   style OS  fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center
-#   style CAOM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center
-#   style AYM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center
-#   style AOM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center
-#   style CAYM fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center
-#   style NOS fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center
-#   style NYS fill:#f1f0f5, stroke:#333, stroke-width:2px, text-align: center
-#   style E   fill:#FFF, stroke:#333, stroke-width:4px, text-align: center
-#   style D   fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center
-#   style GD  fill:#a1f590, stroke:#333, stroke-width:2px, text-align: center"))
-# }
-
+plot_diagram <-
+  function (kin_total, rounding = 3) {
+    rels <- c("ggd", "gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys")
+    vertices <- data.frame(
+      nodes = rels
+      , x = c(1, 1, 1, 1, 1, 1, 1, 0, -1, 0, -1, 2, 3, 2, 3)
+      , y = c(0, 1, 2, 3, 4, 5, 6, 4, 3, 3, 2, 4, 3, 3, 2)
+    )
+    d <- data.frame(from = c("ggd", "gd", "d", "Focal", "m",
+                             "gm", "gm", "oa", "m", "os", "gm", "ya", "m", "ys"),
+                    to = c("gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa",
+                           "os", "nos", "ya", "cya", "ys", "nys"))
+    lookup <- c(with(kin_total, paste0(kin, " \n", round(count, rounding))), "Focal")
+    names(lookup) <- c(kin_total$kin, "Focal")
+    vertices$nodes <- lookup[vertices$nodes]
+    d$from <- lookup[d$from]
+    d$to <- lookup[d$to]
+    # to show full relative names
+    relatives <- c("Cousins from older aunt", "Cousins from younger aunt",
+                   "Daughter", "Grand-daughter", "Great-grand-daughter",
+                   "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister",
+                   "Nieces from younger sister", "Aunt older than mother",
+                   "Aunt younger than mother", "Older sister", "Younger sister", "")
+    names(relatives) <- c("coa", "cya", "d", "gd", "ggd",
+                          "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os",
+                          "ys", "Focal")
+    labs <- relatives[rels]
+    # Plot
+    b <- igraph::graph_from_data_frame(vertices = vertices, d= d, directed = FALSE)
+    b_auto_layout <- igraph::layout.auto(b)
+    b_auto_layout_scaled <- igraph::norm_coords(b_auto_layout, ymin=-1, ymax=1, xmin=-1, xmax=1)
+    plot(
+      b
+      , vertex.size = 70
+      , curved = 1
+      , vertex.color = "#FFF1E2"
+      , vertex.shape = "circle"
+      , vertex.label.cex = 0.8
+      , vertex.label.color = "black"
+      , edge.width = 2
+      , layout = b_auto_layout_scaled * 3
+      , rescale = FALSE
+      , xlim = c(-3.3,3.3)
+      , ylim = c(-2.5,2.5)
+    )
+    # Add relative names
+    # Thanks to Egor Kotov for this tip!
+    plot(
+      b
+      , vertex.size = 70
+      , curved = 1
+      , vertex.color = NA
+      , vertex.shape = "none"
+      , vertex.label = labs
+      , vertex.label.dist = -6.5
+      , vertex.label.cex = 0.8
+      , vertex.label.color = "black"
+      , vertex.label.degree = -pi/2
+      , edge.width = 2
+      , edge.color = NA
+      , layout = b_auto_layout_scaled * 3
+      , rescale = FALSE
+      , xlim = c(-3.3,3.3)
+      , ylim = c(-2.5,2.5)
+      , add = T
+    )
+  }

From 75c1bc3199b46b922c0d66a3bb04a4895ec5a4fa Mon Sep 17 00:00:00 2001
From: alburezg <gatemonte@gmail.com>
Date: Wed, 4 Jan 2023 18:59:15 +0100
Subject: [PATCH 04/89] increased y-limit of igraph

---
 R/plot_diagramm.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/plot_diagramm.R b/R/plot_diagramm.R
index 0305e38..87d55b1 100644
--- a/R/plot_diagramm.R
+++ b/R/plot_diagramm.R
@@ -49,7 +49,7 @@ plot_diagram <-
       , layout = b_auto_layout_scaled * 3
       , rescale = FALSE
       , xlim = c(-3.3,3.3)
-      , ylim = c(-2.5,2.5)
+      , ylim = c(-3.1,3.1)
     )
     # Add relative names
     # Thanks to Egor Kotov for this tip!
@@ -69,7 +69,7 @@ plot_diagram <-
       , layout = b_auto_layout_scaled * 3
       , rescale = FALSE
       , xlim = c(-3.3,3.3)
-      , ylim = c(-2.5,2.5)
+      , ylim = c(-3.1,3.1)
       , add = T
     )
   }

From bcbdcef1e59c204a4d6067ee5be8657b86923734 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Tue, 24 Jan 2023 16:05:08 -0300
Subject: [PATCH 05/89] adding 2sex model + vignette

---
 R/data.R                    |  22 ++++
 R/kin.R                     |   8 +-
 R/kin_time_invariant_2sex.R | 162 +++++++++++++++++++++++
 R/kin_time_variant_2sex.R   | 254 ++++++++++++++++++++++++++++++++++++
 data/fra_asfr_sex.rda       | Bin 0 -> 870 bytes
 data/fra_surv_sex.rda       | Bin 0 -> 1598 bytes
 vignettes/TwoSex.Rmd        | 205 +++++++++++++++++++++++++++++
 7 files changed, 650 insertions(+), 1 deletion(-)
 create mode 100644 R/kin_time_invariant_2sex.R
 create mode 100644 R/kin_time_variant_2sex.R
 create mode 100644 data/fra_asfr_sex.rda
 create mode 100644 data/fra_surv_sex.rda
 create mode 100644 vignettes/TwoSex.Rmd

diff --git a/R/data.R b/R/data.R
index 1bacb90..b11636a 100644
--- a/R/data.R
+++ b/R/data.R
@@ -127,3 +127,25 @@
 #' @source
 #'   Caswell (2021)
 "kin_svk1990_caswell2020"
+
+#' Fertility for France (2012) by sex in Caswell (2022).
+#'
+#' Fertility for France (2012) by sex in Caswell (2022).
+#' @docType data
+#' @format
+#' A data.frame with age specific fertility rates by age and sex.
+#'
+#' @source
+#'   Caswell (2022)
+"fra_asfr_sex"
+
+#' Survival probability for France (2012) by sex in Caswell (2022).
+#'
+#' Survival probability for France (2012) by sex in Caswell (2022).
+#' @docType data
+#' @format
+#' A data.frame with survival probabilities by age and sex.
+#'
+#' @source
+#'   Caswell (2022)
+"fra_surv_sex"
diff --git a/R/kin.R b/R/kin.R
index b585345..6dd8dec 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -74,7 +74,13 @@ kin <- function(U = NULL, f = NULL,
   }
 
   # reorder
-  kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, kin, age_kin, living, dead)
+  kin_full <- kin_full %>%
+    dplyr::select(year, cohort, age_focal, kin, age_kin, living, dead) %>%
+    dplyr::mutate(kin_group =  dplyr::case_when(kin %in% c("ys", "os") ~ "s",
+                                                kin %in% c("ya", "oa") ~ "a",
+                                                kin %in% c("coa", "cya") ~ "c",
+                                                kin %in% c("nys", "nos") ~ "n",
+                                                T ~ kin))
 
   # summary
   # select period/cohort
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
new file mode 100644
index 0000000..1d178b1
--- /dev/null
+++ b/R/kin_time_invariant_2sex.R
@@ -0,0 +1,162 @@
+#' Estimate kin counts in a time invariant framework considering two sex
+
+#' @description Two sex matrix framework for kin count estimates. Implementation of Caswell (2022).
+
+#' @param pf numeric. A vector of survival probabilities for females with same length as ages.
+#' @param ff numeric. A vector of age-specific fertility rates for females with same length as ages.
+#' @param pm numeric. A vector of survival probabilities for males with same length as ages.
+#' @param fm numeric. A vector of age-specific fertility rates for males with same length as ages.
+#' @param sex_focal character. "f" for female or "m" for male.
+#' @param birth_female numeric. Female portion at birth.
+#' @param pif numeric. For using some specific non-stable age distribution of childbearing for mothers (same length as ages). Default `NULL`.
+#' @param pim numeric. For using some specific non-stable age distribution of childbearing for fathers (same length as ages). Default `NULL`.
+#' @param output_kin character. kin to return, considering matrilineal names. For example "m" for parents, "d" for children, etc. See the `vignette` for all kin types.
+#' @param list_output logical. Results as a list with `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE`
+#'
+#' @return A data frame with focal´s age, related ages and type of kin
+#' (for example `d` is children, `oa` is older aunts/uncles, etc.), sex, alive and death. If `list_output = TRUE` then this is a list.
+#' @export
+
+kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
+                                    ff = NULL, fm = NULL,
+                                    sex_focal = "f",
+                                    birth_female = 1/2.04,
+                                    pif = NULL, pim = NULL,
+                                    output_kin = NULL,
+                                    list_output = FALSE){
+
+  # same input length
+  if(!all(length(pf)==length(pm), length(pf)==length(ff), length(pf)==length(fm))) stop("Lengths of p's and f's should be the same")
+
+  # make matrix transition from vectors. Include death counts with matrix M
+  age = 0:(length(pf)-1)
+  ages = length(age)
+  agess = ages * 2
+  Uf = Um = Ff = Fm = Gt = zeros = matrix(0, nrow=ages, ncol=ages)
+  Uf[row(Uf)-1 == col(Uf)] <- pf[-ages]
+  Uf[ages, ages] = Uf[ages]
+  Um[row(Um)-1 == col(Um)] <- pm[-ages]
+  Um[ages, ages] = Um[ages]
+  Mm <- diag(1-pm)
+  Mf <- diag(1-pf)
+  Ut <- as.matrix(rbind(
+        cbind(Matrix::bdiag(Uf, Um), Matrix::bdiag(zeros, zeros)),
+        cbind(Matrix::bdiag(Mf, Mm), Matrix::bdiag(zeros, zeros))))
+  Ff[1,] = ff
+  Fm[1,] = fm
+  Ft <- Ft_star <- matrix(0, agess*2, agess*2)
+  Ft[1:agess,1:agess] <- rbind(cbind(birth_female * Ff, birth_female * Fm),
+                                     cbind((1-birth_female) * Ff, (1-birth_female) * Fm))
+
+  # mother and father do not reproduce independently to produce focal´s siblings. Assign to mother
+  Ft_star[1:agess,1:ages] <- rbind(birth_female * Ff, (1-birth_female) * Ff)
+
+  # parents age distribution under stable assumption in case no input
+  if(is.null(pif)){
+    A = Uf + Ff
+    A_decomp = eigen(A)
+    lambda = as.double(A_decomp$values[1])
+    w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+    pif = w*A[1,]/sum(w*A[1,])
+    if(all(is.na(pif))) pif <- rep(1/ages, ages)
+  }
+  if(is.null(pim)){
+    A = Um + Fm
+    A_decomp = eigen(A)
+    lambda = as.double(A_decomp$values[1])
+    w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+    pim = w*A[1,]/sum(w*A[1,])
+    if(all(is.na(pim))) pim <- rep(1/ages, ages)
+  }
+
+  # initial count matrix (kin ages in rows and focal age in column)
+  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, agess*2, ages)
+
+  # locate focal at age 0 depending sex
+  sex_index <- ifelse(sex_focal == "f", 1, ages+1)
+  phi[sex_index, 1] <- 1
+
+  # G matrix moves focal by age
+  G <- matrix(0, nrow=ages, ncol=ages)
+  G[row(G)-1 == col(G)] <- 1
+  Gt <- matrix(0, agess*2, agess*2)
+  Gt[1:(agess), 1:(agess)] <- as.matrix(Matrix::bdiag(G, G))
+
+  # focal´s trip
+  # names of matrix count by kin refers to matrilineal as general reference
+  m[1:(agess),1] = c(pif, pim)
+  for(i in 1:(ages-1)){
+    # i = 1
+    phi[,i+1] = Gt %*% phi[, i]
+    d[,i+1]   = Ut %*% d[,i] + Ft %*% phi[,i]
+    gd[,i+1]  = Ut %*% gd[,i] + Ft %*% d[,i]
+    ggd[,i+1] = Ut %*% ggd[,i] + Ft %*% gd[,i]
+    m[,i+1]   = Ut %*% m[,i]
+    ys[,i+1]  = Ut %*% ys[,i] + Ft_star %*% m[,i]
+    nys[,i+1] = Ut %*% nys[,i] + Ft %*% ys[,i]
+  }
+
+  gm[1:(agess),1] = m[1:(agess),] %*% (pif + pim)
+  for(i in 1:(ages-1)){
+    gm[,i+1]  = Ut %*% gm[,i]
+  }
+
+  ggm[1:(agess),1] = gm[1:(agess),] %*% (pif + pim)
+  for(i in 1:(ages-1)){
+    ggm[,i+1]  = Ut %*% ggm[,i]
+  }
+
+  os[1:(agess),1]  = d[1:(agess),] %*% pif
+  nos[1:(agess),1] = gd[1:(agess),] %*% pif
+  for(i in 1:(ages-1)){
+    os[,i+1]  = Ut %*% os[,i]
+    nos[,i+1] = Ut %*% nos[,i] + Ft %*% os[,i]
+  }
+
+  oa[1:(agess),1]  = os[1:(agess),] %*% (pif + pim)
+  ya[1:(agess),1]  = ys[1:(agess),] %*% (pif + pim)
+  coa[1:(agess),1] = nos[1:(agess),] %*% (pif + pim)
+  cya[1:(agess),1] = nys[1:(agess),] %*% (pif + pim)
+  for(i in 1:(ages-1)){
+    oa[,i+1]  = Ut %*% oa[,i]
+    ya[,i+1]  = Ut %*% ya[,i]  + Ft_star %*% gm[,i]
+    coa[,i+1] = Ut %*% coa[,i] + Ft %*% oa[,i]
+    cya[,i+1] = Ut %*% cya[,i] + Ft %*% ya[,i]
+  }
+
+  # get results
+  kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
+                   nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
+
+  # only selected kin
+  if(!is.null(output_kin)){
+    kin_list <- kin_list %>% purrr::keep(names(.) %in% output_kin)
+  }
+
+  # as data.frame
+  kin <- purrr::map2(kin_list, names(kin_list),
+                     function(x,y){
+                       out <- as.data.frame(x)
+                       colnames(out) <- age
+                       out %>%
+                         dplyr::mutate(kin = y,
+                                       age_kin = rep(age,4),
+                                       sex = rep(c(rep("f",ages), rep("m",ages)),2),
+                                       alive = c(rep("living",2*ages), rep("dead",2*ages))) %>%
+                         tidyr::pivot_longer(c(-age_kin, -kin, -sex, -alive), names_to = "age_focal", values_to = "count") %>%
+                         dplyr::mutate(age_focal = as.integer(age_focal)) %>%
+                         tidyr::pivot_wider(names_from = alive, values_from = count)
+                     }
+  ) %>%
+    purrr::reduce(rbind)
+
+
+  # results as list?
+  if(list_output) {
+    out <- kin_list
+  }else{
+    out <- kin
+  }
+
+  return(out)
+}
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
new file mode 100644
index 0000000..93248ba
--- /dev/null
+++ b/R/kin_time_variant_2sex.R
@@ -0,0 +1,254 @@
+#' Estimate kin counts in a time variant framework
+
+kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
+                                   Ff = NULL, Fm = NULL,
+                                   sex_focal = "f",
+                                   birth_female = 1/2.04,
+                                   Pif = NULL, Pim = NULL,
+                                   Nf = NULL, Nm = NULL,
+                                   output_cohort = NULL, output_period = NULL, output_kin = NULL,
+                                   list_output = FALSE){
+
+  # same input length
+  if(!all(dim(Pf) == dim(Pm), dim(Pf) == dim(Ff), dim(Pf) == dim(Fm))) stop("Dimension of P's and F's should be the same")
+
+  # data should be from same interval years
+  years_data <- as.integer(colnames(Pf))
+  if(var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again")
+
+  # utils
+  age          <- 0:(nrow(Pf)-1)
+  n_years_data <- length(years_data)
+  ages         <- length(age)
+  agess        <- ages*2
+  om           <- max(age)
+  zeros        <- matrix(0, nrow=ages, ncol=ages)
+
+  # age distribution at childborn
+  if(is.null(Pif)){
+    if(!is.null(Nf)){
+      Pif <- rbind(t(t(Nf * Ff)/colSums(Nf * Ff)), matrix(0,ages,length(years_data)))
+    }else{
+      Pif <- matrix(0, nrow=ages, ncol=n_years_data)
+      no_Pif <- TRUE
+    }
+  }
+  if(is.null(Pim)){
+    if(!is.null(Nm)){
+      Pim <- rbind(t(t(Nm * Fm)/colSums(Nm * Fm)), matrix(0,ages,length(years_data)))
+    }else{
+      Pim <- matrix(0, nrow=ages, ncol=n_years_data)
+      no_Pim <- TRUE
+    }
+  }
+
+  # get lists of matrix
+  Ul = Fl = Fl_star = list()
+  for(t in 1:n_years_data){
+    # t = 1
+    Uf = Um = Fft = Fmt = Mm = Mf = Gt = zeros = matrix(0, nrow=ages, ncol=ages)
+    Uf[row(Uf)-1 == col(Uf)] <- Pf[-ages,t]
+    Uf[ages, ages] = Pf[ages,t]
+    Um[row(Um)-1 == col(Um)] <- Pm[-ages,t]
+    Um[ages, ages] = Pm[ages,t]
+    Mm <- diag(1-Pm[,t])
+    Mf <- diag(1-Pf[,t])
+    Ut <- as.matrix(rbind(
+      cbind(Matrix::bdiag(Uf, Um), Matrix::bdiag(zeros, zeros)),
+      cbind(Matrix::bdiag(Mf, Mm), Matrix::bdiag(zeros, zeros))))
+    Ul[[as.character(years_data[t])]] <- Ut
+    Fft[1,] = Ff[,t]
+    Fmt[1,] = Fm[,t]
+    Ft <- Ft_star <- matrix(0, agess*2, agess*2)
+    Ft[1:agess,1:agess] <- rbind(cbind(birth_female * Fft, birth_female * Fmt),
+                                 cbind((1-birth_female) * Fft, (1-birth_female) * Fmt))
+    Ft_star[1:agess,1:ages] <- rbind(birth_female * Fft, (1-birth_female) * Fft)
+    Fl[[as.character(years_data[t])]] <- Ft
+    Fl_star[[as.character(years_data[t])]] <- Ft_star
+    if(no_Pif){
+      A <- Uf + Fft
+      A_decomp = eigen(A)
+      w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+      Pif[,t] <- w*A[1,]/sum(w*A[1,])
+    }
+    if(no_Pim){
+      A <- Um + Fmt
+      A_decomp = eigen(A)
+      w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+      Pim[,t] <- w*A[1,]/sum(w*A[1,])
+    }
+  }
+
+  # loop over years (more performance here)
+  kin_all <- list()
+  pb <- progress::progress_bar$new(
+    format = "Running over input years [:bar] :percent",
+    total = n_years_data, clear = FALSE, width = 60)
+  for (iyear in 1:n_years_data){
+    # iyear = 1
+    Ut <- as.matrix(Ul[[iyear]])
+    Ft <- as.matrix(Fl[[iyear]])
+    Ft_star <- as.matrix(Fl_star[[iyear]])
+    pitf <- Pif[,iyear]
+    pitm <- Pim[,iyear]
+    pit <- c(pitf, pitm)
+    if (iyear==1){
+      p1f <- Pf[,1]
+      p1m <- Pm[,1]
+      f1f <- Ff[,1]
+      f1m <- Fm[,1]
+      pif1 <- Pif[,1]
+      pim1 <- Pim[,1]
+      kin_all[[1]] <- kin_time_invariant_2sex(pf = p1f, pm = p1m,
+                                              ff = f1f, fm = f1m,
+                                              pif = pif1, pim = pim1,
+                                              birth_female = birth_female, list_output = TRUE)
+    }
+    kin_all[[iyear+1]] <- timevarying_kin_2sex(Ut=Ut, Ft=Ft, Ft_star=Ft_star, pit=pit, sex_focal, ages, pkin=kin_all[[iyear]])
+    pb$tick()
+  }
+
+  # filter years and kin that were selected
+  names(kin_all) <- as.character(years_data)
+
+  # combinations to return
+  out_selected <- output_period_cohort_combination(output_cohort, output_period, age = age, years_data = years_data)
+
+  possible_kin <- c("d","gd","ggd","m","gm","ggm","os","ys","nos","nys","oa","ya","coa","cya")
+  if(is.null(output_kin)){
+    selected_kin_position <- 1:length(possible_kin)
+  }else{
+    selected_kin_position <- which(possible_kin %in% output_kin)
+  }
+
+  # first filter
+  kin_list <- kin_all %>%
+    purrr::keep(names(.) %in% as.character(unique(out_selected$year))) %>%
+    purrr::map(~ .[selected_kin_position])
+
+  # long format
+  kin <- lapply(names(kin_list), function(Y){
+    X <- kin_list[[Y]]
+    X <- purrr::map2(X, names(X), function(x,y){
+      # browser()
+      as.data.frame(x) %>%
+        dplyr::mutate(year = Y,
+                      kin=y,
+                      sex = rep(c(rep("f",ages), rep("m",ages)),2),
+                      age_kin = rep(age,4),
+                      alive = c(rep("living",agess), rep("dead",agess)),
+                      .before=everything())
+      }) %>%
+      dplyr::bind_rows() %>%
+      stats::setNames(c("year","kin", "sex", "age_kin","alive",as.character(age))) %>%
+      tidyr::gather(age_focal, count,-age_kin, -kin, -year, -sex, -alive) %>%
+      dplyr::mutate(age_focal = as.integer(age_focal),
+                    year = as.integer(year),
+                    cohort = year - age_focal) %>%
+      dplyr::filter(age_focal %in% out_selected$age[out_selected$year==as.integer(Y)]) %>%
+      tidyr::pivot_wider(names_from = alive, values_from = count)
+      }) %>%
+    dplyr::bind_rows()
+
+  # results as list?
+  if(list_output) {
+    out <- kin_list
+  }else{
+    out <- kin
+  }
+
+  return(out)
+}
+
+#' one time projection kin
+
+#' @description one time projection kin. internal function.
+#'
+#' @param Ut numeric. A matrix of survival probabilities (or ratios).
+#' @param ft numeric. A matrix of age-specific fertility rates.
+#' @param pit numeric. A matrix with distribution of childbearing.
+#' @param ages numeric.
+#' @param pkin numeric. A list with kin count distribution in previous year.
+#
+timevarying_kin_2sex<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
+
+  agess <- ages*2
+  om <- ages-1
+  pif <- pit[1:ages]
+  pim <- pit[(ages+1):agess]
+  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,agess*2,ages)
+  kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
+                    nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
+
+  # G matrix moves focal by age
+  G <- matrix(0, nrow=ages, ncol=ages)
+  G[row(G)-1 == col(G)] <- 1
+  Gt <- matrix(0, agess*2, agess*2)
+  Gt[1:(agess), 1:(agess)] <- as.matrix(Matrix::bdiag(G, G))
+
+  # locate focal at age 0 depending sex
+  sex_index <- ifelse(sex_focal == "f", 1, ages+1)
+  phi[sex_index, 1] <- 1
+
+  # initial distribution
+  m[1:agess,1]   = pit
+  gm[1:agess,1]  = pkin[["m"]][1:agess,] %*% (pif + pim)
+  ggm[1:agess,1] = pkin[["gm"]][1:agess,] %*% (pif + pim)
+  os[1:agess,1]  = pkin[["d"]][1:agess,] %*% pif
+  nos[1:agess,1] = pkin[["gd"]][1:ages,] %*% pif
+  oa[1:agess,1]  = pkin[["os"]][1:agess,] %*% (pif + pim)
+  ya[1:agess,1]  = pkin[["ys"]][1:agess,] %*% (pif + pim)
+  coa[1:agess,1] = pkin[["nos"]][1:agess,] %*% (pif + pim)
+  cya[1:agess,1] = pkin[["nys"]][1:agess,] %*% (pif + pim)
+
+  for (ix in 1:om){
+    # ix = 1
+    phi[,ix+1] = Gt %*% phi[, ix]
+    d[,ix+1]   = Ut %*% pkin[["d"]][,ix]   + Ft %*% phi[,ix]
+    gd[,ix+1]  = Ut %*% pkin[["gd"]][,ix]  + Ft %*% pkin[["d"]][,ix]
+    ggd[,ix+1] = Ut %*% pkin[["ggd"]][,ix] + Ft %*% pkin[["gd"]][,ix]
+    m[,ix+1]   = Ut %*% pkin[["m"]][,ix]
+    gm[,ix+1]  = Ut %*% pkin[["gm"]][,ix]
+    ggm[,ix+1] = Ut %*% pkin[["ggm"]][,ix]
+    os[,ix+1]  = Ut %*% pkin[["os"]][,ix]
+    ys[,ix+1]  = Ut %*% pkin[["ys"]][,ix]  + Ft_star %*% pkin[["m"]][,ix]
+    nos[,ix+1] = Ut %*% pkin[["nos"]][,ix] + Ft %*% pkin[["os"]][,ix]
+    nys[,ix+1] = Ut %*% pkin[["nys"]][,ix] + Ft %*% pkin[["ys"]][,ix]
+    oa[,ix+1]  = Ut %*% pkin[["oa"]][,ix]
+    ya[,ix+1]  = Ut %*% pkin[["ya"]][,ix]  + Ft_star %*% pkin[["gm"]][,ix]
+    coa[,ix+1] = Ut %*% pkin[["coa"]][,ix] + Ft %*% pkin[["oa"]][,ix]
+    cya[,ix+1] = Ut %*% pkin[["cya"]][,ix] + Ft %*% pkin[["ya"]][,ix]
+  }
+
+  kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
+                    nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
+
+  return(kin_list)
+}
+
+#' defince apc combination to return
+
+#' @description defince apc to return.
+#'
+output_period_cohort_combination <- function(output_cohort = NULL, output_period = NULL, age = NULL, years_data = NULL){
+
+  # no specific
+  if(is.null(output_period) & is.null(output_cohort)){
+    message("No specific output was set. Return all period data.")
+    output_period <- years_data
+  }
+
+  # cohort combination
+  if(!is.null(output_cohort)){
+    selected_cohorts_year_age <- data.frame(age = rep(age,length(output_cohort)),
+                                            year = purrr::map(output_cohort,.f = ~.x+age) %>%
+                                              unlist(use.names = F))
+  }else{selected_cohorts_year_age <- c()}
+
+  # period year combination
+  if(!is.null(output_period)){selected_years_age <- expand.grid(age, output_period) %>% dplyr::rename(age=1,year=2)
+  }else{selected_years_age <- c()}
+
+  # end
+  return(dplyr::bind_rows(selected_years_age,selected_cohorts_year_age) %>% dplyr::distinct())
+}
diff --git a/data/fra_asfr_sex.rda b/data/fra_asfr_sex.rda
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HcmV?d00001

diff --git a/vignettes/TwoSex.Rmd b/vignettes/TwoSex.Rmd
new file mode 100644
index 0000000..32d2c63
--- /dev/null
+++ b/vignettes/TwoSex.Rmd
@@ -0,0 +1,205 @@
+---
+title: "Expected kin counts by type of relative: A matrix implementation"
+output: 
+  html_document:
+    toc: true
+    toc_depth: 1
+vignette: >
+  %\VignetteIndexEntry{Use}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include=FALSE}
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+```
+
+Age distribution of Focal´s father when she born depends on male fertility pattern. Living siblings depends on sex composition (brothers and sisters) due to differential mortality risk. Intensity in care tasks is not the same between sex in many societies, so the sex of ego and his/her "sandwichness" change, because an average family network expects different roles in supporting. For these reasons, and many others, sex specific kin count estimates are important. Here we implement relations in Caswell (2022), not focusing in applications that can be analogous to the one-sex model, but in the specific advantages.
+
+```{r, message=FALSE, warning=FALSE}
+library(DemoKin)
+library(tidyr)
+library(dplyr)
+library(ggplot2)
+library(knitr)
+devtools::load_all()
+```
+
+### 1.1. Rates by sex
+
+Female fertility by age is not a widespread available data source. Caswell (2022) takes Schoumaker (2019) makes available estimates for 160 countries, reporting  that male TFR almost always exceeds female TFR. We take the case of France in 2012 for showing how functions works (fertility and mortality data are available with the package, with column-sex values). Let´s see main differences in age distribution (TFR of 1.98 and 1.99 for males and females, practically the same)
+
+```{r}
+fra_fert_f <- fra_fert_sex[,"ff"]
+fra_fert_m <- fra_fert_sex[,"fm"]
+fra_surv_f <- fra_surv_sex[,"pf"]
+fra_surv_m <- fra_surv_sex[,"pm"]
+sum(fra_fert_m)-sum(fra_fert_f)
+data.frame(value = c(fra_fert_f, fra_fert_m, fra_surv_f, fra_surv_m),
+           age = rep(0:100, 4),
+           sex = rep(c(rep("f", 101), rep("m", 101)), 2),
+           risk = c(rep("fertility rate", 101 * 2), rep("survival probability", 101 * 2))) %>%
+  ggplot(aes(age, value, col=sex)) + geom_line() + facet_wrap(~ risk, scales = "free_y") + theme_bw()
+```
+
+### 1.1. Visualizing the distribution of kin
+
+Compared with one sex functions, here the user needs to specify risk by sex and decide results for which ego´s sex wants. (**this should be wrapped on a kin general formula? (note for Diego)**)
+
+```{r}
+kin_out <- kin_time_invariant_2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
+```
+
+Let´s group aunts and siblings and see living kin by age (**should reply fig 6 (note for Diego)**).
+
+```{r}
+kin_out <- kin_out %>% 
+  mutate(kin = case_when(kin %in% c("s", "s") ~ "s",
+                         kin %in% c("ya", "oa") ~ "a",
+                         T ~ kin)) %>%
+  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a"))
+kin_out %>% 
+  group_by(kin, age_focal, sex) %>%
+  summarise(count=sum(living)) %>%
+  ggplot(aes(age_focal, count, fill=sex))+
+  geom_area()+
+  theme_bw() +
+  facet_wrap(~kin)
+```
+
+Kin availability by sex allows to inspect its distribution, a traditional measure in demography is the sex ratio (with females in denominator). A French woman would expect to have half grandfathers for each grandmother at 25 years old.  
+
+```{r}
+kin_out %>% 
+  group_by(kin, age_focal) %>%
+  summarise(sex_ratio=sum(living[sex=="m"], na.rm=T)/sum(living[sex=="f"], na.rm=T)) %>%
+  ggplot(aes(age_focal, sex_ratio))+
+  geom_line()+
+  theme_bw() +
+  facet_wrap(~kin, scales = "free")
+```
+
+How ego experiences relative deaths depends mainly on how wide is the sex-gap in mortality. She starts to lose fathers earlier than mothers. The difference on the level by sex in grandparents is due to initial availability by sex.
+
+```{r}
+# sex ratio
+kin_out %>%
+  group_by(kin, sex, age_focal) %>%
+  summarise(count=sum(dead)) %>%
+  ggplot(aes(age_focal, count, col=sex))+
+  geom_line()+
+  theme_bw() +
+  facet_wrap(~kin)
+```
+
+### 2 Approximations
+
+Caswell (2022) mentions some ways to approximate to 2-sex distribution of living kins. Here we compare the full 2-sex model that introduced before with *androgynous* variant (male fertility and survival are the same as females) and the use of GKP factors. The first comparison can be done by age, having very similar results in this case, except for grandfathers and great-grandfathers who transits higher ages and sex-gap is higher.
+
+```{r}
+kin_out <- kin_time_invariant_2sex(fra_surv_f, fra_surv_m, 
+                                   fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
+kin_out_androgynous <- kin_time_invariant_2sex(fra_surv_f, fra_surv_f, 
+                                          fra_fert_f, fra_fert_f, sex_focal = "f", birth_female = .5)
+bind_rows(
+  kin_out %>% mutate(type = "full"),
+  kin_out_androgynous %>% mutate(type = "androgynous")) %>%
+  group_by(kin, age_focal, sex, type) %>%
+  summarise(count = sum(living)) %>%
+  ggplot(aes(age_focal, count, linetype = type)) +
+  geom_line() +
+  theme_bw() +
+  theme(legend.position = "bottom", axis.text.x = element_blank()) +
+  facet_grid(row = vars(sex), col = vars(kin), scales = "free")
+```
+
+Now we can multiply results from 1-sex model by the GKP factors by kin, to obtain a simple but very consistent approximation of totals (both sex) at different ages of Focal.  
+
+```{r}
+# with gkp
+kin_out_1sex <- kin(fra_surv_f, fra_fert_f, birth_female = .5)
+kin_out_GKP <- kin_out_1sex$kin_full %>%
+  mutate(living = case_when(kin == "m" ~ living * 2,
+                            kin == "gm" ~ living * 4,
+                            kin == "ggm" ~ living * 8,
+                            kin == "d" ~ living * 2,
+                            kin == "gd" ~ living * 4,
+                            kin == "ggd" ~ living * 4,
+                            kin == "oa" ~ living * 4,
+                            kin == "ya" ~ living * 4,
+                            kin == "os" ~ living * 2,
+                            kin == "ys" ~ living * 2,
+                            kin == "coa" ~ living * 8,
+                            kin == "cya" ~ living * 8,
+                            kin == "nos" ~ living * 4,
+                            kin == "nys" ~ living * 4))
+
+bind_rows(
+  kin_out %>% mutate(type = "full"),
+  kin_out_androgynous %>% mutate(type = "androgynous"),
+  kin_out_GKP %>% mutate(type = "gkp")) %>%
+  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
+                          kin %in% c("ya", "oa") ~ "a",
+                          kin %in% c("coa", "cya") ~ "c",
+                          kin %in% c("nys", "nos") ~ "n",
+                          T ~ kin)) %>%
+  filter(age_focal %in% c(5, 15, 30, 60, 80)) %>%
+  group_by(kin, age_focal, type) %>%
+  summarise(count = sum(living)) %>%
+  ggplot(aes(type, count)) +
+  geom_bar(aes(fill=type), stat = "identity") +
+  theme_bw()+theme(axis.text.x = element_text(angle = 90), legend.position = "bottom")+
+  facet_grid(col = vars(kin), row = vars(age_focal), scales = "free")
+```
+ 
+### 2 Time variant
+
+But Focal will see his/her tree developing with current risk, being part of the evolving demographic transition, in any of its stages. Let´s compare what would be living kin for Swedish female if she would experienced time varying rates instead of period ones from 1950. We can use data already loaded in the package.
+
+```{r}
+years <- ncol(swe_px)
+ages <- nrow(swe_px)
+swe_surv_f_matrix <- swe_px
+swe_surv_m_matrix <- swe_px ^ 1.5 # this could be replaced with downloaded data from UN
+swe_fert_f_matrix <- swe_asfr
+swe_fert_m_matrix <- rbind(matrix(0, 5, years), 
+            swe_asfr[-((ages-4):ages),]) * 1.05
+par(mfrow=c(1,2))
+main("Sweden year 1900")
+plot(swe_surv_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Survival probability")
+lines(swe_surv_m_matrix[,"1900"], col=2)
+plot(swe_fert_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Fertility rate")
+lines(swe_fert_m_matrix[,"1900"], col=2)
+```
+There is a n increase of living relatives because of mortality improvements, very small for grandparents because main advantages in health conditions made a huge effect in infant mortality first. Less children would have this woman in case of varying rates, due to fertility transition in the first decades in Sweden.
+
+```{r}
+kin_out_time_invariant <- kin_time_invariant_2sex(
+                      swe_surv_f_matrix[,"1900"], swe_surv_m_matrix[,"1900"], 
+                      swe_fert_f_matrix[,"1900"], swe_fert_m_matrix[,"1900"], 
+                      sex_focal = "f", birth_female = .5)
+kin_out_time_variant <- kin_time_variant_2sex(
+                      swe_surv_f_matrix, swe_surv_m_matrix,
+                      swe_fert_f_matrix, swe_fert_m_matrix,
+                      sex_focal = "f",
+                      birth_female = .5,
+                      output_cohort = 1900)
+
+kin_out_time_variant %>%
+  filter(cohort == 1900) %>% mutate(type = "variant") %>%
+  bind_rows(kin_out_time_invariant %>% mutate(type = "invariant")) %>% 
+  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
+                         kin %in% c("ya", "oa") ~ "a",
+                         T ~ kin)) %>%
+  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a")) %>%
+  group_by(type, kin, age_focal, sex) %>%
+  summarise(count=sum(living)) %>%
+  ggplot(aes(age_focal, count, linetype=type))+
+  geom_line()+ theme_bw() +
+  facet_grid(cols = vars(kin), rows=vars(sex), scales = "free")
+```
+
+ 
+## References
+
+Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359–396. 

From 972d5c10b195688a5500064cbd7bee700ee39f77 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 27 Jan 2023 08:52:17 -0300
Subject: [PATCH 06/89] general 2sexkin

---
 R/kin2sex.R          | 124 +++++++++++++++++++++++++++++++++++++++++++
 vignettes/TwoSex.Rmd |  73 ++++++++++++-------------
 2 files changed, 159 insertions(+), 38 deletions(-)
 create mode 100644 R/kin2sex.R

diff --git a/R/kin2sex.R b/R/kin2sex.R
new file mode 100644
index 0000000..dee1254
--- /dev/null
+++ b/R/kin2sex.R
@@ -0,0 +1,124 @@
+#' Estimate kin counts in a two-sex framework
+
+#' @description Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework.
+#' @details See Caswell (2022) for details on formulas.
+#' @param U numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param f numeric. Same as U but for fertility rates.
+#' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
+#' @param N numeric. Same as U but for population distribution (counts or `%`). Optional.
+#' @param pi numeric. Same as U but for childbearing distribution (sum to 1). Optional.
+#' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
+#' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
+#' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
+#' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
+#' @param stable logic. Deprecated. Use `time_invariant`.
+#' @return A list with:
+#' \itemize{
+#'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age.}
+#'  \item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending on the given arguments):}
+#'  {\itemize{
+#'  \item{`count_living`}{: count of living kin at actual age of Focal}
+#'  \item{`mean_age`}{: mean age of each type of living kin.}
+#'  \item{`sd_age`}{: standard deviation of age of each type of living kin.}
+#'  \item{`count_death`}{: count of dead kin at specific age of Focal.}
+#'  \item{`count_cum_death`}{: cumulated count of dead kin until specific age of Focal.}
+#'  \item{`mean_age_lost`}{: mean age where Focal lost her relative.}
+#'  }
+#'  }
+#' }
+
+#' @export
+#'
+# get kin ----------------------------------------------------------------
+kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
+                 time_invariant = TRUE,
+                 sex_focal = "f",
+                 birth_female = 1/2.04,
+                 pif = NULL, pim = NULL,
+                 output_cohort = NULL, output_period = NULL, output_kin=NULL)
+  {
+
+  age <- as.integer(rownames(pf))
+  years_data <- as.integer(colnames(pf))
+
+  # kin to return
+  all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
+  if(is.null(output_kin)){
+    output_kin <- all_possible_kin
+  }else{
+    output_kin <- match.arg(tolower(output_kin), all_possible_kin, several.ok = TRUE)
+  }
+
+  # if time dependent or not
+  if(time_invariant){
+      if(!is.vector(pf)) {
+        output_period <- min(years_data)
+        pf <- pf[,as.character(output_period)]
+        pm <- pm[,as.character(output_period)]
+        ff <- ff[,as.character(output_period)]
+        fm <- fm[,as.character(output_period)]
+      }
+      kin_full <- kin_time_invariant_2sex(pf, pm, ff, fm,
+                                     sex_focal = sex_focal,
+                                     birth_female = birth_female,
+                                     pif = pif, pim = pim,
+                                     output_kin = output_kin) %>%
+                              dplyr::mutate(cohort = NA, year = NA)
+  }else{
+      if(!is.null(output_cohort) & !is.null(output_period)) stop("sorry, you can not select cohort and period. Choose one please")
+      kin_full <- kin_time_variant_2sex(Pf = pf, Pm = pm,
+                                   Ff = ff, Fm = fm,
+                                   sex_focal = sex_focal,
+                                   birth_female = birth_female,
+                                   Pif = pif, Pim = pim,
+                                   output_cohort = output_cohort, output_period = output_period,
+                                   output_kin = output_kin)
+      message(paste0("Assuming stable population before ", min(years_data), "."))
+  }
+
+  # reorder
+  kin_full <- kin_full %>%
+    dplyr::select(year, cohort, age_focal, sex, kin, age_kin, living, dead) %>%
+    dplyr::mutate(kin_group =  dplyr::case_when(kin %in% c("ys", "os") ~ "s",
+                                                kin %in% c("ya", "oa") ~ "a",
+                                                kin %in% c("coa", "cya") ~ "c",
+                                                kin %in% c("nys", "nos") ~ "n",
+                                                T ~ kin))
+
+  # summary
+  # select period/cohort
+  if(!is.null(output_cohort)){
+    agrupar <- "cohort"
+  } else if(!is.null(output_period)){
+    agrupar <- "year"
+  } else{
+    agrupar <- c("year", "cohort")
+  }
+  agrupar_no_age_focal <- c("kin", "sex", agrupar)
+  agrupar <- c("age_focal", "kin", "sex", agrupar)
+
+  kin_summary <- dplyr::bind_rows(
+    kin_full %>%
+      dplyr::rename(total=living) %>%
+      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
+      dplyr::summarise(count_living = sum(total),
+                mean_age = sum(total*age_kin)/sum(total),
+                sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
+      tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", "value"),
+    kin_full %>%
+      dplyr::rename(total=dead) %>%
+      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
+      dplyr::summarise(count_dead = sum(total)) %>%
+      dplyr::ungroup() %>%
+      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar_no_age_focal))) %>%
+      dplyr::mutate(count_cum_dead = cumsum(count_dead),
+                    mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>%
+      dplyr::ungroup() %>%
+      tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", "value")) %>%
+      dplyr::ungroup() %>%
+      tidyr::pivot_wider(names_from = indicator, values_from = value)
+
+    # return
+    kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
+  return(kin_out)
+}
diff --git a/vignettes/TwoSex.Rmd b/vignettes/TwoSex.Rmd
index 32d2c63..3245293 100644
--- a/vignettes/TwoSex.Rmd
+++ b/vignettes/TwoSex.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "Expected kin counts by type of relative: A matrix implementation"
+title: "TwoExpected kin counts by type of relative: A matrix implementation"
 output: 
   html_document:
     toc: true
@@ -47,20 +47,20 @@ data.frame(value = c(fra_fert_f, fra_fert_m, fra_surv_f, fra_surv_m),
 Compared with one sex functions, here the user needs to specify risk by sex and decide results for which ego´s sex wants. (**this should be wrapped on a kin general formula? (note for Diego)**)
 
 ```{r}
-kin_out <- kin_time_invariant_2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
+kin_out <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
 ```
 
 Let´s group aunts and siblings and see living kin by age (**should reply fig 6 (note for Diego)**).
 
 ```{r}
-kin_out <- kin_out %>% 
+kin_out <- kin_out$kin_summary %>% 
   mutate(kin = case_when(kin %in% c("s", "s") ~ "s",
                          kin %in% c("ya", "oa") ~ "a",
                          T ~ kin)) %>%
   filter(kin %in% c("d", "m", "gm", "ggm", "s", "a"))
 kin_out %>% 
   group_by(kin, age_focal, sex) %>%
-  summarise(count=sum(living)) %>%
+  summarise(count=sum(count_living)) %>%
   ggplot(aes(age_focal, count, fill=sex))+
   geom_area()+
   theme_bw() +
@@ -72,7 +72,7 @@ Kin availability by sex allows to inspect its distribution, a traditional measur
 ```{r}
 kin_out %>% 
   group_by(kin, age_focal) %>%
-  summarise(sex_ratio=sum(living[sex=="m"], na.rm=T)/sum(living[sex=="f"], na.rm=T)) %>%
+  summarise(sex_ratio=sum(count_living[sex=="m"], na.rm=T)/sum(count_living[sex=="f"], na.rm=T)) %>%
   ggplot(aes(age_focal, sex_ratio))+
   geom_line()+
   theme_bw() +
@@ -85,7 +85,7 @@ How ego experiences relative deaths depends mainly on how wide is the sex-gap in
 # sex ratio
 kin_out %>%
   group_by(kin, sex, age_focal) %>%
-  summarise(count=sum(dead)) %>%
+  summarise(count=sum(count_dead)) %>%
   ggplot(aes(age_focal, count, col=sex))+
   geom_line()+
   theme_bw() +
@@ -97,15 +97,13 @@ kin_out %>%
 Caswell (2022) mentions some ways to approximate to 2-sex distribution of living kins. Here we compare the full 2-sex model that introduced before with *androgynous* variant (male fertility and survival are the same as females) and the use of GKP factors. The first comparison can be done by age, having very similar results in this case, except for grandfathers and great-grandfathers who transits higher ages and sex-gap is higher.
 
 ```{r}
-kin_out <- kin_time_invariant_2sex(fra_surv_f, fra_surv_m, 
-                                   fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
-kin_out_androgynous <- kin_time_invariant_2sex(fra_surv_f, fra_surv_f, 
-                                          fra_fert_f, fra_fert_f, sex_focal = "f", birth_female = .5)
+kin_out <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
+kin_out_androgynous <- kin2sex(fra_surv_f, fra_surv_f, fra_fert_f, fra_fert_f, sex_focal = "f", birth_female = .5)
 bind_rows(
-  kin_out %>% mutate(type = "full"),
-  kin_out_androgynous %>% mutate(type = "androgynous")) %>%
+  kin_out$kin_summary %>% mutate(type = "full"),
+  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous")) %>%
   group_by(kin, age_focal, sex, type) %>%
-  summarise(count = sum(living)) %>%
+  summarise(count = sum(count_living)) %>%
   ggplot(aes(age_focal, count, linetype = type)) +
   geom_line() +
   theme_bw() +
@@ -118,25 +116,25 @@ Now we can multiply results from 1-sex model by the GKP factors by kin, to obtai
 ```{r}
 # with gkp
 kin_out_1sex <- kin(fra_surv_f, fra_fert_f, birth_female = .5)
-kin_out_GKP <- kin_out_1sex$kin_full %>%
-  mutate(living = case_when(kin == "m" ~ living * 2,
-                            kin == "gm" ~ living * 4,
-                            kin == "ggm" ~ living * 8,
-                            kin == "d" ~ living * 2,
-                            kin == "gd" ~ living * 4,
-                            kin == "ggd" ~ living * 4,
-                            kin == "oa" ~ living * 4,
-                            kin == "ya" ~ living * 4,
-                            kin == "os" ~ living * 2,
-                            kin == "ys" ~ living * 2,
-                            kin == "coa" ~ living * 8,
-                            kin == "cya" ~ living * 8,
-                            kin == "nos" ~ living * 4,
-                            kin == "nys" ~ living * 4))
+kin_out_GKP <- kin_out_1sex$kin_summary%>%
+  mutate(count_living = case_when(kin == "m" ~ count_living * 2,
+                            kin == "gm" ~ count_living * 4,
+                            kin == "ggm" ~ count_living * 8,
+                            kin == "d" ~ count_living * 2,
+                            kin == "gd" ~ count_living * 4,
+                            kin == "ggd" ~ count_living * 4,
+                            kin == "oa" ~ count_living * 4,
+                            kin == "ya" ~ count_living * 4,
+                            kin == "os" ~ count_living * 2,
+                            kin == "ys" ~ count_living * 2,
+                            kin == "coa" ~ count_living * 8,
+                            kin == "cya" ~ count_living * 8,
+                            kin == "nos" ~ count_living * 4,
+                            kin == "nys" ~ count_living * 4))
 
 bind_rows(
-  kin_out %>% mutate(type = "full"),
-  kin_out_androgynous %>% mutate(type = "androgynous"),
+  kin_out$kin_summary %>% mutate(type = "full"),
+  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous"),
   kin_out_GKP %>% mutate(type = "gkp")) %>%
   mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
                           kin %in% c("ya", "oa") ~ "a",
@@ -145,7 +143,7 @@ bind_rows(
                           T ~ kin)) %>%
   filter(age_focal %in% c(5, 15, 30, 60, 80)) %>%
   group_by(kin, age_focal, type) %>%
-  summarise(count = sum(living)) %>%
+  summarise(count = sum(count_living)) %>%
   ggplot(aes(type, count)) +
   geom_bar(aes(fill=type), stat = "identity") +
   theme_bw()+theme(axis.text.x = element_text(angle = 90), legend.position = "bottom")+
@@ -165,7 +163,6 @@ swe_fert_f_matrix <- swe_asfr
 swe_fert_m_matrix <- rbind(matrix(0, 5, years), 
             swe_asfr[-((ages-4):ages),]) * 1.05
 par(mfrow=c(1,2))
-main("Sweden year 1900")
 plot(swe_surv_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Survival probability")
 lines(swe_surv_m_matrix[,"1900"], col=2)
 plot(swe_fert_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Fertility rate")
@@ -174,26 +171,26 @@ lines(swe_fert_m_matrix[,"1900"], col=2)
 There is a n increase of living relatives because of mortality improvements, very small for grandparents because main advantages in health conditions made a huge effect in infant mortality first. Less children would have this woman in case of varying rates, due to fertility transition in the first decades in Sweden.
 
 ```{r}
-kin_out_time_invariant <- kin_time_invariant_2sex(
+kin_out_time_invariant <- kin2sex(
                       swe_surv_f_matrix[,"1900"], swe_surv_m_matrix[,"1900"], 
                       swe_fert_f_matrix[,"1900"], swe_fert_m_matrix[,"1900"], 
                       sex_focal = "f", birth_female = .5)
-kin_out_time_variant <- kin_time_variant_2sex(
+kin_out_time_variant <- kin2sex(
                       swe_surv_f_matrix, swe_surv_m_matrix,
                       swe_fert_f_matrix, swe_fert_m_matrix,
-                      sex_focal = "f",
+                      sex_focal = "f",time_invariant = FALSE,
                       birth_female = .5,
                       output_cohort = 1900)
 
-kin_out_time_variant %>%
+kin_out_time_variant$kin_summary %>%
   filter(cohort == 1900) %>% mutate(type = "variant") %>%
-  bind_rows(kin_out_time_invariant %>% mutate(type = "invariant")) %>% 
+  bind_rows(kin_out_time_invariant$kin_summary %>% mutate(type = "invariant")) %>% 
   mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
                          kin %in% c("ya", "oa") ~ "a",
                          T ~ kin)) %>%
   filter(kin %in% c("d", "m", "gm", "ggm", "s", "a")) %>%
   group_by(type, kin, age_focal, sex) %>%
-  summarise(count=sum(living)) %>%
+  summarise(count=sum(count_living)) %>%
   ggplot(aes(age_focal, count, linetype=type))+
   geom_line()+ theme_bw() +
   facet_grid(cols = vars(kin), rows=vars(sex), scales = "free")

From 47ade3080d1f8e3c253c3d584994460dbbbfbbf9 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Sun, 29 Jan 2023 12:14:52 -0300
Subject: [PATCH 07/89] document 2sex

---
 DESCRIPTION                             |   2 +-
 NAMESPACE                               |   3 +
 R/kin.R                                 |  82 ++++++++------
 R/kin2sex.R                             |  40 ++++---
 R/kin_time_invariant.R                  |  31 +++--
 R/kin_time_invariant_2sex.R             |  12 +-
 R/kin_time_variant.R                    | 103 ++++++++---------
 R/kin_time_variant_2sex.R               | 145 +++++++++++++-----------
 data/fra_asfr_sex.rda                   | Bin 870 -> 870 bytes
 man/fra_asfr_sex.Rd                     |  19 ++++
 man/fra_surv_sex.Rd                     |  19 ++++
 man/kin.Rd                              |  40 +++++--
 man/kin2sex.Rd                          |  86 ++++++++++++++
 man/kin_time_invariant.Rd               |  10 +-
 man/kin_time_invariant_2sex.Rd          |  52 +++++++++
 man/kin_time_variant.Rd                 |  17 +--
 man/kin_time_variant_2sex.Rd            |  65 +++++++++++
 man/output_period_cohort_combination.Rd |  11 +-
 man/timevarying_kin_2sex.Rd             |  22 ++++
 vignettes/Reference.Rmd                 |  15 +--
 vignettes/TwoSex.Rmd                    |  31 ++---
 21 files changed, 566 insertions(+), 239 deletions(-)
 create mode 100644 man/fra_asfr_sex.Rd
 create mode 100644 man/fra_surv_sex.Rd
 create mode 100644 man/kin2sex.Rd
 create mode 100644 man/kin_time_invariant_2sex.Rd
 create mode 100644 man/kin_time_variant_2sex.Rd
 create mode 100644 man/timevarying_kin_2sex.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 9b3e9db..2e5402c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -21,7 +21,6 @@ Imports:
     dplyr,
     tidyr,
     purrr,
-    forcats,
     HMDHFDplus,
     progress,
     matrixcalc,
@@ -30,6 +29,7 @@ Imports:
     stats,
     igraph,
     magrittr,
+    data.table,
     lifecycle
 BugReports: https://github.com/IvanWilli/DemoKin/issues
 Depends: 
diff --git a/NAMESPACE b/NAMESPACE
index f5c7de1..5502931 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -4,9 +4,12 @@ export("%>%")
 export(demokin_codes)
 export(get_HMDHFD)
 export(kin)
+export(kin2sex)
 export(kin_multi_stage)
 export(kin_time_invariant)
+export(kin_time_invariant_2sex)
 export(kin_time_variant)
+export(kin_time_variant_2sex)
 export(plot_diagram)
 export(rename_kin)
 importFrom(magrittr,"%>%")
diff --git a/R/kin.R b/R/kin.R
index 6dd8dec..866f1c9 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -1,21 +1,26 @@
-#' Estimate kin counts
+#' Estimate kin counts in a one-sex framework.
 
-#' @description Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework.
+#' @description Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. This produce a matrilineal (or patrilineal)
+#' kin count distribution by kin and age.
 #' @details See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by default).
-#' @param U numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
-#' @param f numeric. Same as U but for fertility rates.
+#' @param p numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class
+#' in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param f numeric. Same as p but for fertility rates.
 #' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
-#' @param N numeric. Same as U but for population distribution (counts or `%`). Optional.
+#' @param n numeric. Same as p but for population distribution (counts or `%`). Optional.
 #' @param pi numeric. Same as U but for childbearing distribution (sum to 1). Optional.
 #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
-#' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
+#' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring,
+#' this needs to be set as 1.
 #' @param stable logic. Deprecated. Use `time_invariant`.
 #' @return A list with:
 #' \itemize{
-#'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age.}
-#'  \item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending on the given arguments):}
+#'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter,
+#'  `oa` is older aunts, etc.), including living and dead kin at that age.}
+#'  \item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`,
+#'  grouping by cohort or period (depending on the given arguments):}
 #'  {\itemize{
 #'  \item{`count_living`}{: count of living kin at actual age of Focal}
 #'  \item{`mean_age`}{: mean age of each type of living kin.}
@@ -26,25 +31,35 @@
 #'  }
 #'  }
 #' }
-
 #' @export
-#'
-# get kin ----------------------------------------------------------------
-kin <- function(U = NULL, f = NULL,
-                 time_invariant = TRUE,
-                 N = NULL, pi = NULL,
-                 output_cohort = NULL, output_period = NULL, output_kin=NULL,
-                 birth_female = 1/2.04,
-                 stable = lifecycle::deprecated())
-  {
+#' @examples
+#' \dontrun{
+#' # Kin expected matrilineal count for a Swedish female based on 2015 rates.
+#' swe_surv_2015 <- swe_px[,"2015"]
+#' swe_asfr_2015 <- swe_asfr[,"2015"]
+#' # Run kinship models
+#' swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015)
+#' head(swe_2015)
+#'}
 
-  age <- as.integer(rownames(U))
-  years_data <- as.integer(colnames(U))
+kin <- function(p = NULL, f = NULL,
+                time_invariant = TRUE,
+                pi = NULL, n = NULL,
+                output_cohort = NULL, output_period = NULL, output_kin=NULL,
+                birth_female = 1/2.04,
+                stable = lifecycle::deprecated(),
+                U = lifecycle::deprecated())
+  {
 
+  # changed arguments
   if (lifecycle::is_present(stable)) {
     lifecycle::deprecate_warn("0.0.0.9000", "kin(stable)", details = "Used time_invariant")
     time_invariant <- stable
   }
+  if (lifecycle::is_present(U)) {
+    lifecycle::deprecate_warn("0.0.0.9000", "kin(stable)", details = "Used time_invariant")
+    p <- U
+  }
 
   # kin to return
   all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
@@ -54,35 +69,27 @@ kin <- function(U = NULL, f = NULL,
     output_kin <- match.arg(tolower(output_kin), all_possible_kin, several.ok = TRUE)
   }
 
-  # if time dependent or not
+  # if is time dependent or not
+  age <- as.integer(rownames(p))
+  years_data <- as.integer(colnames(p))
   if(time_invariant){
-      if(!is.vector(U)) {
+      if(!is.vector(p)) {
         output_period <- min(years_data)
-        U <- U[,as.character(output_period)]
+        p <- p[,as.character(output_period)]
         f <- f[,as.character(output_period)]
       }
-      kin_full <- kin_time_invariant(U = U, f = f,
+      kin_full <- kin_time_invariant(p = p, f = f,
                                      output_kin = output_kin, birth_female = birth_female) %>%
                               dplyr::mutate(cohort = NA, year = NA)
   }else{
       if(!is.null(output_cohort) & !is.null(output_period)) stop("sorry, you can not select cohort and period. Choose one please")
-      kin_full <- kin_time_variant(U = U, f = f, N = N, pi = pi,
+      kin_full <- kin_time_variant(p = p, f = f, pi = pi, n = n,
                               output_cohort = output_cohort, output_period = output_period,
                               output_kin = output_kin,
                               birth_female = birth_female)
       message(paste0("Assuming stable population before ", min(years_data), "."))
   }
 
-  # reorder
-  kin_full <- kin_full %>%
-    dplyr::select(year, cohort, age_focal, kin, age_kin, living, dead) %>%
-    dplyr::mutate(kin_group =  dplyr::case_when(kin %in% c("ys", "os") ~ "s",
-                                                kin %in% c("ya", "oa") ~ "a",
-                                                kin %in% c("coa", "cya") ~ "c",
-                                                kin %in% c("nys", "nos") ~ "n",
-                                                T ~ kin))
-
-  # summary
   # select period/cohort
   if(!is.null(output_cohort)){
     agrupar <- "cohort"
@@ -94,6 +101,7 @@ kin <- function(U = NULL, f = NULL,
   agrupar_no_age_focal <- c("kin", agrupar)
   agrupar <- c("age_focal", "kin", agrupar)
 
+  # get summary indicators based on group variables
   kin_summary <- dplyr::bind_rows(
     kin_full %>%
       dplyr::rename(total=living) %>%
@@ -115,7 +123,7 @@ kin <- function(U = NULL, f = NULL,
       dplyr::ungroup() %>%
       tidyr::pivot_wider(names_from = indicator, values_from = value)
 
-    # return
-    kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
+  # return
+  kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
   return(kin_out)
 }
diff --git a/R/kin2sex.R b/R/kin2sex.R
index dee1254..aa46eba 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -1,12 +1,19 @@
 #' Estimate kin counts in a two-sex framework
 
-#' @description Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework.
+#' @description Implementation of two-sex matrix kinship model. This produces kin counts grouped by kin, age and sex of
+#' each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
+#' are grouped in one male count of cousins.
 #' @details See Caswell (2022) for details on formulas.
-#' @param U numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
-#' @param f numeric. Same as U but for fertility rates.
+#' @param pf numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param pm numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param ff numeric. Same as pf but for fertility rates.
+#' @param fm numeric. Same as pm but for fertility rates.
 #' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
-#' @param N numeric. Same as U but for population distribution (counts or `%`). Optional.
-#' @param pi numeric. Same as U but for childbearing distribution (sum to 1). Optional.
+#' @param sex_focal character. "f" for female or "m" for male.
+#' @param pif numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default `NULL`.
+#' @param pim numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default `NULL`.
+#' @param nf numeric. Same as pf but for population distribution (counts or `%`). Optional.
+#' @param nm numeric. Same as pm but for population distribution (counts or `%`). Optional.
 #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
@@ -15,7 +22,7 @@
 #' @return A list with:
 #' \itemize{
 #'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age.}
-#'  \item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending on the given arguments):}
+#'  \item{kin_summary}{ a data frame with Focal´s age, related ages, sex and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending on the given arguments):}
 #'  {\itemize{
 #'  \item{`count_living`}{: count of living kin at actual age of Focal}
 #'  \item{`mean_age`}{: mean age of each type of living kin.}
@@ -26,8 +33,13 @@
 #'  }
 #'  }
 #' }
-
 #' @export
+#' @examples
+#' \dontrun{
+#' # Kin expected count by relative sex for a French female based on 2012 rates.
+#' fra_2012 <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m)
+#' head(fra_2012)
+#'}
 #'
 # get kin ----------------------------------------------------------------
 kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
@@ -35,6 +47,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
                  sex_focal = "f",
                  birth_female = 1/2.04,
                  pif = NULL, pim = NULL,
+                 nf = NULL, nm = NULL,
                  output_cohort = NULL, output_period = NULL, output_kin=NULL)
   {
 
@@ -66,11 +79,12 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
                               dplyr::mutate(cohort = NA, year = NA)
   }else{
       if(!is.null(output_cohort) & !is.null(output_period)) stop("sorry, you can not select cohort and period. Choose one please")
-      kin_full <- kin_time_variant_2sex(Pf = pf, Pm = pm,
-                                   Ff = ff, Fm = fm,
+      kin_full <- kin_time_variant_2sex(pf = pf, pm = pm,
+                                   ff = ff, fm = fm,
                                    sex_focal = sex_focal,
                                    birth_female = birth_female,
-                                   Pif = pif, Pim = pim,
+                                   pif = pif, pim = pim,
+                                   nf = nf, nm = nm,
                                    output_cohort = output_cohort, output_period = output_period,
                                    output_kin = output_kin)
       message(paste0("Assuming stable population before ", min(years_data), "."))
@@ -78,7 +92,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
 
   # reorder
   kin_full <- kin_full %>%
-    dplyr::select(year, cohort, age_focal, sex, kin, age_kin, living, dead) %>%
+    dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead) %>%
     dplyr::mutate(kin_group =  dplyr::case_when(kin %in% c("ys", "os") ~ "s",
                                                 kin %in% c("ya", "oa") ~ "a",
                                                 kin %in% c("coa", "cya") ~ "c",
@@ -94,8 +108,8 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   } else{
     agrupar <- c("year", "cohort")
   }
-  agrupar_no_age_focal <- c("kin", "sex", agrupar)
-  agrupar <- c("age_focal", "kin", "sex", agrupar)
+  agrupar_no_age_focal <- c("kin", "sex_kin", agrupar)
+  agrupar <- c("age_focal", "kin", "sex_kin", agrupar)
 
   kin_summary <- dplyr::bind_rows(
     kin_full %>%
diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R
index d843c4e..2f85412 100644
--- a/R/kin_time_invariant.R
+++ b/R/kin_time_invariant.R
@@ -1,37 +1,37 @@
-#' Estimate kin counts in a time invariant framework
+#' Estimate kin counts in a time invariant framework for one-sex model (matrilineal/patrilineal)
 
-#' @description Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019).
+#' @description Mtrix implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019).
 
-#' @param U numeric. A vector of survival probabilities with same length as ages.
+#' @param p numeric. A vector of survival probabilities with same length as ages.
 #' @param f numeric. A vector of age-specific fertility rates with same length as ages.
 #' @param birth_female numeric. Female portion at birth.
 #' @param pi numeric. For using some specific non-stable age distribution of childbearing (same length as ages). Default `NULL`.
-#' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types.
+#' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See `vignette` for all kin types.
 #' @param list_output logical. Results as a list with `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE`
 #'
 #' @return A data frame with focal´s age, related ages and type of kin
 #' (for example `d` is daughter, `oa` is older aunts, etc.), alive and death. If `list_output = TRUE` then this is a list.
 #' @export
 
-kin_time_invariant <- function(U = NULL, f = NULL,
+kin_time_invariant <- function(p = NULL, f = NULL,
                         birth_female = 1/2.04,
                         pi = NULL,
                         output_kin = NULL,
                         list_output = FALSE){
 
   # make matrix transition from vectors
-  age = 0:(length(U)-1)
+  age = 0:(length(p)-1)
   ages = length(age)
-  Ut = Mt = zeros = Dcum = matrix(0, nrow=ages, ncol=ages)
-  Ut[row(Ut)-1 == col(Ut)] <- U[-ages]
-  Ut[ages, ages] = U[ages]
-  diag(Mt) = 1 - U
+  Ut = Mt = zeros = matrix(0, nrow=ages, ncol=ages)
+  Ut[row(Ut)-1 == col(Ut)] <- p[-ages]
+  Ut[ages, ages] = p[ages]
+  diag(Mt) = 1 - p
   Ut = rbind(cbind(Ut,zeros),
-             cbind(Mt,Dcum))
+             cbind(Mt,zeros))
   ft = matrix(0, nrow=ages*2, ncol=ages*2)
   ft[1,1:ages] = f * birth_female
 
-  # stable age distr
+  # stable age distribution in case no pi is given
   if(is.null(pi)){
     A = Ut[1:ages,1:ages] + ft[1:ages,1:ages]
     A_decomp = eigen(A)
@@ -57,24 +57,20 @@ kin_time_invariant <- function(U = NULL, f = NULL,
     ys[,i+1]  = Ut %*% ys[,i] + ft %*% m[,i]
     nys[,i+1] = Ut %*% nys[,i] + ft %*% ys[,i]
   }
-
   gm[1:ages,1] = m[1:ages,] %*% pi
   for(i in 1:(ages-1)){
     gm[,i+1]  = Ut %*% gm[,i]
   }
-
   ggm[1:ages,1] = gm[1:ages,] %*% pi
   for(i in 1:(ages-1)){
     ggm[,i+1]  = Ut %*% ggm[,i]
   }
-
   os[1:ages,1]  = d[1:ages,] %*% pi
   nos[1:ages,1] = gd[1:ages,] %*% pi
   for(i in 1:(ages-1)){
     os[,i+1]  = Ut %*% os[,i]
     nos[,i+1] = Ut %*% nos[,i] + ft %*% os[,i]
   }
-
   oa[1:ages,1]  = os[1:ages,] %*% pi
   ya[1:ages,1]  = ys[1:ages,] %*% pi
   coa[1:ages,1] = nos[1:ages,] %*% pi
@@ -95,7 +91,7 @@ kin_time_invariant <- function(U = NULL, f = NULL,
     kin_list <- kin_list %>% purrr::keep(names(.) %in% output_kin)
   }
 
-  # as data.frame
+  # reshape as data.frame
   kin <- purrr::map2(kin_list, names(kin_list),
                function(x,y){
                     out <- as.data.frame(x)
@@ -118,6 +114,5 @@ kin_time_invariant <- function(U = NULL, f = NULL,
     }else{
       out <- kin
     }
-
   return(out)
 }
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index 1d178b1..f864154 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -1,7 +1,9 @@
-#' Estimate kin counts in a time invariant framework considering two sex
-
-#' @description Two sex matrix framework for kin count estimates. Implementation of Caswell (2022).
+#' Estimate kin counts in a time invariant framework for two-sex model.
 
+#' @description Two-sex matrix framework for kin count estimates.This produces kin counts grouped by kin, age and sex of
+#' each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
+#' are grouped in one male count of cousins.
+#' @details See Caswell (2022) for details on formulas.
 #' @param pf numeric. A vector of survival probabilities for females with same length as ages.
 #' @param ff numeric. A vector of age-specific fertility rates for females with same length as ages.
 #' @param pm numeric. A vector of survival probabilities for males with same length as ages.
@@ -141,9 +143,9 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
                        out %>%
                          dplyr::mutate(kin = y,
                                        age_kin = rep(age,4),
-                                       sex = rep(c(rep("f",ages), rep("m",ages)),2),
+                                       sex_kin = rep(c(rep("f",ages), rep("m",ages)),2),
                                        alive = c(rep("living",2*ages), rep("dead",2*ages))) %>%
-                         tidyr::pivot_longer(c(-age_kin, -kin, -sex, -alive), names_to = "age_focal", values_to = "count") %>%
+                         tidyr::pivot_longer(c(-age_kin, -kin, -sex_kin, -alive), names_to = "age_focal", values_to = "count") %>%
                          dplyr::mutate(age_focal = as.integer(age_focal)) %>%
                          tidyr::pivot_wider(names_from = alive, values_from = count)
                      }
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index bc962fc..c73f1ec 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -1,8 +1,8 @@
-#' Estimate kin counts in a time variant framework
+#' Estimate kin counts in a time variant framework (dynamic rates) for one-sex model (matrilineal/patrilineal)
 
-#' @description Implementation of time variant Goodman-Keyfitz-Pullum equations based on Caswell (2021).
-#'
-#' @param U numeric. A matrix of survival ratios with rows as ages and columns as years. Column names must be equal interval.
+#' @description Matrix implementation of time variant Goodman-Keyfitz-Pullum equations in a matrix framework.
+#' @details See Caswell (2021) for details on formulas.
+#' @param p numeric. A matrix of survival ratios with rows as ages and columns as years. Column names must be equal interval.
 #' @param f numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. Coincident with `U`.
 #' @param N numeric. A matrix of population with rows as ages and columns as years. Coincident with `U`.
 #' @param pi numeric. A matrix with distribution of childbearing with rows as ages and columns as years. Coincident with `U`.
@@ -16,22 +16,22 @@
 #' (for example `d` is daughter, `oa` is older aunts, etc.), living and death kin counts, and age of (living or time deceased) relatives. If `list_output = TRUE` then this is a list.
 #' @export
 
-kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL,
+kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
                             output_cohort = NULL, output_period = NULL, output_kin = NULL,
                             birth_female = 1/2.04, list_output = FALSE){
 
   # check input
-  if(is.null(U) | is.null(f)) stop("You need values on U and/or f.")
+  if(is.null(p) | is.null(f)) stop("You need values on p and f.")
 
   # diff years
-  if(!any(as.integer(colnames(U)) == as.integer(colnames(f)))) stop("Data should be from same years.")
+  if(!any(as.integer(colnames(p)) == as.integer(colnames(f)))) stop("Data should be from same years.")
 
   # data should be from same interval years
-  years_data <- as.integer(colnames(U))
+  years_data <- as.integer(colnames(p))
   if(var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again")
 
   # utils
-  age          <- 0:(nrow(U)-1)
+  age          <- 0:(nrow(p)-1)
   n_years_data <- length(years_data)
   ages         <- length(age)
   om           <- max(age)
@@ -39,55 +39,46 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL,
 
   # age distribution at childborn
   if(is.null(pi)){
-    if(is.null(N)){
+    if(is.null(n)){
       # create pi and fill it during the loop
       message("Stable assumption was made for calculating pi on each year because no input data.")
       pi <- matrix(0, nrow=ages, ncol=n_years_data)
     }else{
-      pi <- rbind(t(t(N * f)/colSums(N * f)), matrix(0,ages,length(years_data)))
+      pi <- rbind(t(t(n * f)/colSums(n * f)), matrix(0,ages,length(years_data)))
     }
   }
 
-  # get lists of matrix
-  Ul = fl = list()
-  for(t in 1:n_years_data){
-    Ut = Mt = Dcum = matrix(0, nrow=ages, ncol=ages)
-    Ut[row(Ut)-1 == col(Ut)] <- U[-ages,t]
-    Ut[ages, ages]=U[ages,t]
-    diag(Mt) = 1 - U[,t]
-    Ul[[as.character(years_data[t])]] <- rbind(cbind(Ut,zeros),cbind(Mt,Dcum))
-    ft = matrix(0, nrow=ages*2, ncol=ages*2)
-    ft[1,1:ages] = f[,t] * birth_female
-    fl[[as.character(years_data[t])]] <- ft
-  }
-  U <- Ul
-  f <- fl
-
   # loop over years (more performance here)
   kin_all <- list()
   pb <- progress::progress_bar$new(
     format = "Running over input years [:bar] :percent",
-    total = n_years_data, clear = FALSE, width = 60)
-  for (iyear in 1:n_years_data){
-    # print(iyear)
-    Ut <- as.matrix(U[[iyear]])
-    ft <- as.matrix(f[[iyear]])
+    total = n_years_data + 1, clear = FALSE, width = 50)
+  for (t in 1:n_years_data){
+    # build matrix
+    Ut = Mt = matrix(0, nrow=ages, ncol=ages)
+    Ut[row(Ut)-1 == col(Ut)] <- p[-ages,t]
+    Ut[ages, ages] = p[ages,t]
+    diag(Mt) = 1 - p[,t]
+    Ut = rbind(cbind(Ut,zeros),cbind(Mt,zeros))
+    ft = matrix(0, nrow=ages*2, ncol=ages*2)
+    ft[1,1:ages] = f[,t] * birth_female
     if(is.null(pi)){
       A <- Ut[1:ages,1:ages] + ft[1:ages,1:ages]
       A_decomp = eigen(A)
       w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
-      pit <- pi[,iyear] <- w*A[1,]/sum(w*A[1,])
+      pit <- pi[,t] <- w*A[1,]/sum(w*A[1,])
     }else{
-      pit <- pi[,iyear]
+      pit <- pi[,t]
     }
-    if (iyear==1){
-      U1 <- c(diag(Ut[-1,])[1:om],Ut[om,om])
+    # proj
+    if (t==1){
+      p1 <- c(diag(Ut[-1,])[1:om],Ut[om,om])
       f1 <- ft[1,][1:ages]
       pi1 <- pit[1:ages]
-      kin_all[[1]] <- kin_time_invariant(U = U1, f = f1/birth_female, pi = pi1, birth_female = birth_female,
+      kin_all[[1]] <- kin_time_invariant(p = p1, f = f1/birth_female, pi = pi1, birth_female = birth_female,
                                          list_output = TRUE)
     }
-    kin_all[[iyear+1]] <- timevarying_kin(Ut=Ut,ft=ft,pit=pit,ages,pkin=kin_all[[iyear]])
+    kin_all[[t+1]] <- timevarying_kin(Ut=Ut,ft=ft,pit=pit,ages,pkin=kin_all[[t]])
     pb$tick()
   }
 
@@ -110,23 +101,26 @@ kin_time_variant <- function(U = NULL, f = NULL, N = NULL, pi = NULL,
     purrr::map(~ .[selected_kin_position])
 
   # long format
-  kin <- lapply(names(kin_list), function(Y){
+  kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
-    X <- purrr::map2(X, names(X), function(x,y) as.data.frame(x) %>%
-                dplyr::mutate(year = Y,
-                        kin=y,
-                        age_kin = rep(age,2),
-                        alive = c(rep("living",ages), rep("dead",ages)),
-                        .before=everything())) %>%
-      dplyr::bind_rows() %>%
-      stats::setNames(c("year","kin","age_kin","alive",as.character(age))) %>%
-      tidyr::gather(age_focal, count,-age_kin, -kin, -year, -alive) %>%
-      dplyr::mutate(age_focal = as.integer(age_focal),
-                     year = as.integer(year),
-                     cohort = year - age_focal) %>%
-      dplyr::filter(age_focal %in% out_selected$age[out_selected$year==as.integer(Y)]) %>%
-      tidyr::pivot_wider(names_from = alive, values_from = count)}) %>%
-    dplyr::bind_rows()
+    X <- purrr::map2(X, names(X), function(x,y){
+      x <- as.data.frame(x)
+      x$year <- Y
+      x$kin <- y
+      x$age_kin <- rep(age,2)
+      x$alive <- c(rep("living",ages), rep("dead",ages))
+      return(x)
+      }) %>%
+      data.table::rbindlist() %>%
+      stats::setNames(c(as.character(age), "year","kin","age_kin","alive")) %>%
+      data.table::melt(id.vars = c("year","kin","age_kin","alive"), variable.name = "age_focal", value.name = "count")
+    X$age_focal = as.integer(as.character(X$age_focal))
+    X$year = as.integer(X$year)
+    X$cohort = X$year - X$age_focal
+    X[X$age_focal %in% out_selected$age[out_selected$year==as.integer(Y)],] %>%
+      data.table::dcast(year + kin + age_kin + age_focal + cohort ~ alive, value.var = "count")
+    }) %>% data.table::rbindlist()
+  pb$tick()
 
   # results as list?
   if(list_output) {
@@ -172,7 +166,8 @@ timevarying_kin<- function(Ut, ft, pit, ages, pkin){
   coa[1:ages,1]= pkin[["nos"]][1:ages,] %*% pit[1:ages]
   cya[1:ages,1]= pkin[["nys"]][1:ages,] %*% pit[1:ages]
 
-  for (ix in 1:om){
+  # vers1
+  for(ix in 1:om){
     d[,ix+1]   = Ut %*% pkin[["d"]][,ix]   + ft %*% I[,ix]
     gd[,ix+1]  = Ut %*% pkin[["gd"]][,ix]  + ft %*% pkin[["d"]][,ix]
     ggd[,ix+1] = Ut %*% pkin[["ggd"]][,ix] + ft %*% pkin[["gd"]][,ix]
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index 93248ba..0ffe306 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -1,23 +1,45 @@
-#' Estimate kin counts in a time variant framework
+#' Estimate kin counts in a time variant framework (dynamic rates) in a two-sex framework (Caswell, 2022)
 
-kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
-                                   Ff = NULL, Fm = NULL,
+#' @description Two-sex matrix framework for kin count estimates with varying rates.
+#' This produces kin counts grouped by kin, age and sex of each relatives at each Focal´s age.
+#' For example, male cousins from aunts and uncles from different sibling's parents are grouped in one male count of cousins.
+#' @details See Caswell (2022) for details on formulas.
+#' @param pf numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param pm numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param ff numeric. Same as pf but for fertility rates.
+#' @param fm numeric. Same as pm but for fertility rates.
+#' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
+#' @param sex_focal character. "f" for female or "m" for male.
+#' @param pif numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default `NULL`.
+#' @param pim numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default `NULL`.
+#' @param nf numeric. Same as pf but for population distribution (counts or `%`). Optional.
+#' @param nm numeric. Same as pm but for population distribution (counts or `%`). Optional.
+#' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
+#' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
+#' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
+#' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
+#' @param stable logic. Deprecated. Use `time_invariant`.
+#' @return A data.frame with year, cohort, Focal´s age, related ages, sex and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age and sex.
+#' @export
+
+kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
+                                   ff = NULL, fm = NULL,
                                    sex_focal = "f",
                                    birth_female = 1/2.04,
-                                   Pif = NULL, Pim = NULL,
-                                   Nf = NULL, Nm = NULL,
+                                   pif = NULL, pim = NULL,
+                                   nf = NULL, nm = NULL,
                                    output_cohort = NULL, output_period = NULL, output_kin = NULL,
                                    list_output = FALSE){
 
   # same input length
-  if(!all(dim(Pf) == dim(Pm), dim(Pf) == dim(Ff), dim(Pf) == dim(Fm))) stop("Dimension of P's and F's should be the same")
+  if(!all(dim(pf) == dim(pm), dim(pf) == dim(ff), dim(pf) == dim(fm))) stop("Dimension of P's and F's should be the same")
 
   # data should be from same interval years
-  years_data <- as.integer(colnames(Pf))
+  years_data <- as.integer(colnames(pf))
   if(var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again")
 
   # utils
-  age          <- 0:(nrow(Pf)-1)
+  age          <- 0:(nrow(pf)-1)
   n_years_data <- length(years_data)
   ages         <- length(age)
   agess        <- ages*2
@@ -25,17 +47,19 @@ kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
   zeros        <- matrix(0, nrow=ages, ncol=ages)
 
   # age distribution at childborn
-  if(is.null(Pif)){
-    if(!is.null(Nf)){
-      Pif <- rbind(t(t(Nf * Ff)/colSums(Nf * Ff)), matrix(0,ages,length(years_data)))
+  Pif <- pif
+  Pim <- pim
+  if(is.null(pif)){
+    if(!is.null(nf)){
+      Pif <- rbind(t(t(nf * ff)/colSums(nf * ff)), matrix(0,ages,length(years_data)))
     }else{
       Pif <- matrix(0, nrow=ages, ncol=n_years_data)
       no_Pif <- TRUE
     }
   }
-  if(is.null(Pim)){
-    if(!is.null(Nm)){
-      Pim <- rbind(t(t(Nm * Fm)/colSums(Nm * Fm)), matrix(0,ages,length(years_data)))
+  if(is.null(pim)){
+    if(!is.null(nm)){
+      Pim <- rbind(t(t(nm * fm)/colSums(nm * fm)), matrix(0,ages,length(years_data)))
     }else{
       Pim <- matrix(0, nrow=ages, ncol=n_years_data)
       no_Pim <- TRUE
@@ -44,21 +68,25 @@ kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
 
   # get lists of matrix
   Ul = Fl = Fl_star = list()
+  kin_all <- list()
+  pb <- progress::progress_bar$new(
+    format = "Running over input years [:bar] :percent",
+    total = n_years_data + 1, clear = FALSE, width = 60)
   for(t in 1:n_years_data){
     # t = 1
     Uf = Um = Fft = Fmt = Mm = Mf = Gt = zeros = matrix(0, nrow=ages, ncol=ages)
-    Uf[row(Uf)-1 == col(Uf)] <- Pf[-ages,t]
-    Uf[ages, ages] = Pf[ages,t]
-    Um[row(Um)-1 == col(Um)] <- Pm[-ages,t]
-    Um[ages, ages] = Pm[ages,t]
-    Mm <- diag(1-Pm[,t])
-    Mf <- diag(1-Pf[,t])
+    Uf[row(Uf)-1 == col(Uf)] <- pf[-ages,t]
+    Uf[ages, ages] = pf[ages,t]
+    Um[row(Um)-1 == col(Um)] <- pm[-ages,t]
+    Um[ages, ages] = pm[ages,t]
+    Mm <- diag(1-pm[,t])
+    Mf <- diag(1-pf[,t])
     Ut <- as.matrix(rbind(
       cbind(Matrix::bdiag(Uf, Um), Matrix::bdiag(zeros, zeros)),
       cbind(Matrix::bdiag(Mf, Mm), Matrix::bdiag(zeros, zeros))))
     Ul[[as.character(years_data[t])]] <- Ut
-    Fft[1,] = Ff[,t]
-    Fmt[1,] = Fm[,t]
+    Fft[1,] = ff[,t]
+    Fmt[1,] = fm[,t]
     Ft <- Ft_star <- matrix(0, agess*2, agess*2)
     Ft[1:agess,1:agess] <- rbind(cbind(birth_female * Fft, birth_female * Fmt),
                                  cbind((1-birth_female) * Fft, (1-birth_female) * Fmt))
@@ -77,26 +105,18 @@ kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
       w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
       Pim[,t] <- w*A[1,]/sum(w*A[1,])
     }
-  }
-
-  # loop over years (more performance here)
-  kin_all <- list()
-  pb <- progress::progress_bar$new(
-    format = "Running over input years [:bar] :percent",
-    total = n_years_data, clear = FALSE, width = 60)
-  for (iyear in 1:n_years_data){
-    # iyear = 1
-    Ut <- as.matrix(Ul[[iyear]])
-    Ft <- as.matrix(Fl[[iyear]])
-    Ft_star <- as.matrix(Fl_star[[iyear]])
-    pitf <- Pif[,iyear]
-    pitm <- Pim[,iyear]
+    # project
+    Ut <- as.matrix(Ul[[t]])
+    Ft <- as.matrix(Fl[[t]])
+    Ft_star <- as.matrix(Fl_star[[t]])
+    pitf <- Pif[,t]
+    pitm <- Pim[,t]
     pit <- c(pitf, pitm)
-    if (iyear==1){
-      p1f <- Pf[,1]
-      p1m <- Pm[,1]
-      f1f <- Ff[,1]
-      f1m <- Fm[,1]
+    if (t==1){
+      p1f <- pf[,1]
+      p1m <- pm[,1]
+      f1f <- ff[,1]
+      f1m <- fm[,1]
       pif1 <- Pif[,1]
       pim1 <- Pim[,1]
       kin_all[[1]] <- kin_time_invariant_2sex(pf = p1f, pm = p1m,
@@ -104,7 +124,7 @@ kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
                                               pif = pif1, pim = pim1,
                                               birth_female = birth_female, list_output = TRUE)
     }
-    kin_all[[iyear+1]] <- timevarying_kin_2sex(Ut=Ut, Ft=Ft, Ft_star=Ft_star, pit=pit, sex_focal, ages, pkin=kin_all[[iyear]])
+    kin_all[[t+1]] <- timevarying_kin_2sex(Ut=Ut, Ft=Ft, Ft_star=Ft_star, pit=pit, sex_focal, ages, pkin=kin_all[[t]])
     pb$tick()
   }
 
@@ -125,30 +145,28 @@ kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
   kin_list <- kin_all %>%
     purrr::keep(names(.) %in% as.character(unique(out_selected$year))) %>%
     purrr::map(~ .[selected_kin_position])
-
   # long format
-  kin <- lapply(names(kin_list), function(Y){
+  kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
     X <- purrr::map2(X, names(X), function(x,y){
-      # browser()
-      as.data.frame(x) %>%
-        dplyr::mutate(year = Y,
-                      kin=y,
-                      sex = rep(c(rep("f",ages), rep("m",ages)),2),
-                      age_kin = rep(age,4),
-                      alive = c(rep("living",agess), rep("dead",agess)),
-                      .before=everything())
-      }) %>%
-      dplyr::bind_rows() %>%
-      stats::setNames(c("year","kin", "sex", "age_kin","alive",as.character(age))) %>%
-      tidyr::gather(age_focal, count,-age_kin, -kin, -year, -sex, -alive) %>%
-      dplyr::mutate(age_focal = as.integer(age_focal),
-                    year = as.integer(year),
-                    cohort = year - age_focal) %>%
-      dplyr::filter(age_focal %in% out_selected$age[out_selected$year==as.integer(Y)]) %>%
-      tidyr::pivot_wider(names_from = alive, values_from = count)
-      }) %>%
-    dplyr::bind_rows()
+      x <- as.data.frame(x)
+      x$year <- Y
+      x$kin <- y
+      x$sex_kin <- rep(c(rep("f",ages), rep("m",ages)),2)
+      x$age_kin <- rep(age,2)
+      x$alive <- c(rep("living",ages), rep("dead",ages))
+      return(x)
+    }) %>%
+      data.table::rbindlist() %>%
+      stats::setNames(c(as.character(age), "year","kin","sex_kin","age_kin","alive")) %>%
+      data.table::melt(id.vars = c("year","kin","sex_kin","age_kin","alive"), variable.name = "age_focal", value.name = "count")
+    X$age_focal = as.integer(as.character(X$age_focal))
+    X$year = as.integer(X$year)
+    X$cohort = X$year - X$age_focal
+    X <- X[X$age_focal %in% out_selected$age[out_selected$year==as.integer(Y)],]
+    X <- data.table::dcast(X, year + kin + sex_kin + age_kin + age_focal + cohort ~ alive, value.var = "count", fun.aggregate = sum)
+  }) %>% data.table::rbindlist()
+  pb$tick()
 
   # results as list?
   if(list_output) {
@@ -156,7 +174,6 @@ kin_time_variant_2sex <- function(Pf = NULL, Pm = NULL,
   }else{
     out <- kin
   }
-
   return(out)
 }
 
diff --git a/data/fra_asfr_sex.rda b/data/fra_asfr_sex.rda
index 63188d388eb82c71a020c2674c246758335941c6..557249902db3e2f3da9aaf4ec4037085b3f7653c 100644
GIT binary patch
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diff --git a/man/fra_asfr_sex.Rd b/man/fra_asfr_sex.Rd
new file mode 100644
index 0000000..dfda668
--- /dev/null
+++ b/man/fra_asfr_sex.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{fra_asfr_sex}
+\alias{fra_asfr_sex}
+\title{Fertility for France (2012) by sex in Caswell (2022).}
+\format{
+A data.frame with age specific fertility rates by age and sex.
+}
+\source{
+Caswell (2022)
+}
+\usage{
+fra_asfr_sex
+}
+\description{
+Fertility for France (2012) by sex in Caswell (2022).
+}
+\keyword{datasets}
diff --git a/man/fra_surv_sex.Rd b/man/fra_surv_sex.Rd
new file mode 100644
index 0000000..afa550f
--- /dev/null
+++ b/man/fra_surv_sex.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{fra_surv_sex}
+\alias{fra_surv_sex}
+\title{Survival probability for France (2012) by sex in Caswell (2022).}
+\format{
+A data.frame with survival probabilities by age and sex.
+}
+\source{
+Caswell (2022)
+}
+\usage{
+fra_surv_sex
+}
+\description{
+Survival probability for France (2012) by sex in Caswell (2022).
+}
+\keyword{datasets}
diff --git a/man/kin.Rd b/man/kin.Rd
index bccb33e..036bdd9 100644
--- a/man/kin.Rd
+++ b/man/kin.Rd
@@ -2,47 +2,52 @@
 % Please edit documentation in R/kin.R
 \name{kin}
 \alias{kin}
-\title{Estimate kin counts}
+\title{Estimate kin counts in a one-sex framework.}
 \usage{
 kin(
-  U = NULL,
+  p = NULL,
   f = NULL,
   time_invariant = TRUE,
-  N = NULL,
   pi = NULL,
+  n = NULL,
   output_cohort = NULL,
   output_period = NULL,
   output_kin = NULL,
   birth_female = 1/2.04,
-  stable = lifecycle::deprecated()
+  stable = lifecycle::deprecated(),
+  U = lifecycle::deprecated()
 )
 }
 \arguments{
-\item{U}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+\item{p}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class
+in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
 
-\item{f}{numeric. Same as U but for fertility rates.}
+\item{f}{numeric. Same as p but for fertility rates.}
 
 \item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Default \code{TRUE}.}
 
-\item{N}{numeric. Same as U but for population distribution (counts or \verb{\%}). Optional.}
-
 \item{pi}{numeric. Same as U but for childbearing distribution (sum to 1). Optional.}
 
+\item{n}{numeric. Same as p but for population distribution (counts or \verb{\%}). Optional.}
+
 \item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.}
 
 \item{output_period}{integer. Vector of period years for returning results. Should be within input data years range.}
 
 \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
 
-\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring, this needs to be set as 1.}
+\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring,
+this needs to be set as 1.}
 
 \item{stable}{logic. Deprecated. Use \code{time_invariant}.}
 }
 \value{
 A list with:
 \itemize{
-\item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), including living and dead kin at that age.}
-\item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending on the given arguments):}
+\item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example \code{d} is daughter,
+\code{oa} is older aunts, etc.), including living and dead kin at that age.}
+\item{kin_summary}{ a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing \code{kin_full},
+grouping by cohort or period (depending on the given arguments):}
 {\itemize{
 \item{\code{count_living}}{: count of living kin at actual age of Focal}
 \item{\code{mean_age}}{: mean age of each type of living kin.}
@@ -55,8 +60,19 @@ A list with:
 }
 }
 \description{
-Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework.
+Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. This produce a matrilineal (or patrilineal)
+kin count distribution by kin and age.
 }
 \details{
 See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by default).
 }
+\examples{
+\dontrun{
+# Kin expected matrilineal count for a Swedish female based on 2015 rates.
+swe_surv_2015 <- swe_px[,"2015"]
+swe_asfr_2015 <- swe_asfr[,"2015"]
+# Run kinship models
+swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015)
+head(swe_2015)
+}
+}
diff --git a/man/kin2sex.Rd b/man/kin2sex.Rd
new file mode 100644
index 0000000..34bf7c1
--- /dev/null
+++ b/man/kin2sex.Rd
@@ -0,0 +1,86 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin2sex.R
+\name{kin2sex}
+\alias{kin2sex}
+\title{Estimate kin counts in a two-sex framework}
+\usage{
+kin2sex(
+  pf = NULL,
+  pm = NULL,
+  ff = NULL,
+  fm = NULL,
+  time_invariant = TRUE,
+  sex_focal = "f",
+  birth_female = 1/2.04,
+  pif = NULL,
+  pim = NULL,
+  nf = NULL,
+  nm = NULL,
+  output_cohort = NULL,
+  output_period = NULL,
+  output_kin = NULL
+)
+}
+\arguments{
+\item{pf}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+
+\item{pm}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+
+\item{ff}{numeric. Same as pf but for fertility rates.}
+
+\item{fm}{numeric. Same as pm but for fertility rates.}
+
+\item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Default \code{TRUE}.}
+
+\item{sex_focal}{character. "f" for female or "m" for male.}
+
+\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring, this needs to be set as 1.}
+
+\item{pif}{numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default \code{NULL}.}
+
+\item{pim}{numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default \code{NULL}.}
+
+\item{nf}{numeric. Same as pf but for population distribution (counts or \verb{\%}). Optional.}
+
+\item{nm}{numeric. Same as pm but for population distribution (counts or \verb{\%}). Optional.}
+
+\item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.}
+
+\item{output_period}{integer. Vector of period years for returning results. Should be within input data years range.}
+
+\item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
+
+\item{stable}{logic. Deprecated. Use \code{time_invariant}.}
+}
+\value{
+A list with:
+\itemize{
+\item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), including living and dead kin at that age.}
+\item{kin_summary}{ a data frame with Focal´s age, related ages, sex and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending on the given arguments):}
+{\itemize{
+\item{\code{count_living}}{: count of living kin at actual age of Focal}
+\item{\code{mean_age}}{: mean age of each type of living kin.}
+\item{\code{sd_age}}{: standard deviation of age of each type of living kin.}
+\item{\code{count_death}}{: count of dead kin at specific age of Focal.}
+\item{\code{count_cum_death}}{: cumulated count of dead kin until specific age of Focal.}
+\item{\code{mean_age_lost}}{: mean age where Focal lost her relative.}
+}
+}
+}
+}
+\description{
+Implementation of two-sex matrix kinship model. This produces kin counts grouped by kin, age and sex of
+each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
+are grouped in one male count of cousins.
+}
+\details{
+See Caswell (2022) for details on formulas.
+}
+\examples{
+\dontrun{
+# Kin expected count by relative sex for a French female based on 2012 rates.
+fra_2012 <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m)
+head(fra_2012)
+}
+
+}
diff --git a/man/kin_time_invariant.Rd b/man/kin_time_invariant.Rd
index a470503..d04e243 100644
--- a/man/kin_time_invariant.Rd
+++ b/man/kin_time_invariant.Rd
@@ -2,10 +2,10 @@
 % Please edit documentation in R/kin_time_invariant.R
 \name{kin_time_invariant}
 \alias{kin_time_invariant}
-\title{Estimate kin counts in a time invariant framework}
+\title{Estimate kin counts in a time invariant framework for one-sex model (matrilineal/patrilineal)}
 \usage{
 kin_time_invariant(
-  U = NULL,
+  p = NULL,
   f = NULL,
   birth_female = 1/2.04,
   pi = NULL,
@@ -14,7 +14,7 @@ kin_time_invariant(
 )
 }
 \arguments{
-\item{U}{numeric. A vector of survival probabilities with same length as ages.}
+\item{p}{numeric. A vector of survival probabilities with same length as ages.}
 
 \item{f}{numeric. A vector of age-specific fertility rates with same length as ages.}
 
@@ -22,7 +22,7 @@ kin_time_invariant(
 
 \item{pi}{numeric. For using some specific non-stable age distribution of childbearing (same length as ages). Default \code{NULL}.}
 
-\item{output_kin}{character. kin to return. For example "m" for mother, "d" for daughter. See the \code{vignette} for all kin types.}
+\item{output_kin}{character. kin to return. For example "m" for mother, "d" for daughter. See \code{vignette} for all kin types.}
 
 \item{list_output}{logical. Results as a list with \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
 }
@@ -31,5 +31,5 @@ A data frame with focal´s age, related ages and type of kin
 (for example \code{d} is daughter, \code{oa} is older aunts, etc.), alive and death. If \code{list_output = TRUE} then this is a list.
 }
 \description{
-Implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019).
+Mtrix implementation of Goodman-Keyfitz-Pullum equations adapted by Caswell (2019).
 }
diff --git a/man/kin_time_invariant_2sex.Rd b/man/kin_time_invariant_2sex.Rd
new file mode 100644
index 0000000..550a331
--- /dev/null
+++ b/man/kin_time_invariant_2sex.Rd
@@ -0,0 +1,52 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_time_invariant_2sex.R
+\name{kin_time_invariant_2sex}
+\alias{kin_time_invariant_2sex}
+\title{Estimate kin counts in a time invariant framework for two-sex model.}
+\usage{
+kin_time_invariant_2sex(
+  pf = NULL,
+  pm = NULL,
+  ff = NULL,
+  fm = NULL,
+  sex_focal = "f",
+  birth_female = 1/2.04,
+  pif = NULL,
+  pim = NULL,
+  output_kin = NULL,
+  list_output = FALSE
+)
+}
+\arguments{
+\item{pf}{numeric. A vector of survival probabilities for females with same length as ages.}
+
+\item{pm}{numeric. A vector of survival probabilities for males with same length as ages.}
+
+\item{ff}{numeric. A vector of age-specific fertility rates for females with same length as ages.}
+
+\item{fm}{numeric. A vector of age-specific fertility rates for males with same length as ages.}
+
+\item{sex_focal}{character. "f" for female or "m" for male.}
+
+\item{birth_female}{numeric. Female portion at birth.}
+
+\item{pif}{numeric. For using some specific non-stable age distribution of childbearing for mothers (same length as ages). Default \code{NULL}.}
+
+\item{pim}{numeric. For using some specific non-stable age distribution of childbearing for fathers (same length as ages). Default \code{NULL}.}
+
+\item{output_kin}{character. kin to return, considering matrilineal names. For example "m" for parents, "d" for children, etc. See the \code{vignette} for all kin types.}
+
+\item{list_output}{logical. Results as a list with \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
+}
+\value{
+A data frame with focal´s age, related ages and type of kin
+(for example \code{d} is children, \code{oa} is older aunts/uncles, etc.), sex, alive and death. If \code{list_output = TRUE} then this is a list.
+}
+\description{
+Two-sex matrix framework for kin count estimates.This produces kin counts grouped by kin, age and sex of
+each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
+are grouped in one male count of cousins.
+}
+\details{
+See Caswell (2022) for details on formulas.
+}
diff --git a/man/kin_time_variant.Rd b/man/kin_time_variant.Rd
index da16ad7..17c1f85 100644
--- a/man/kin_time_variant.Rd
+++ b/man/kin_time_variant.Rd
@@ -2,13 +2,13 @@
 % Please edit documentation in R/kin_time_variant.R
 \name{kin_time_variant}
 \alias{kin_time_variant}
-\title{Estimate kin counts in a time variant framework}
+\title{Estimate kin counts in a time variant framework (dynamic rates) for one-sex model (matrilineal/patrilineal)}
 \usage{
 kin_time_variant(
-  U = NULL,
+  p = NULL,
   f = NULL,
-  N = NULL,
   pi = NULL,
+  n = NULL,
   output_cohort = NULL,
   output_period = NULL,
   output_kin = NULL,
@@ -17,12 +17,10 @@ kin_time_variant(
 )
 }
 \arguments{
-\item{U}{numeric. A matrix of survival ratios with rows as ages and columns as years. Column names must be equal interval.}
+\item{p}{numeric. A matrix of survival ratios with rows as ages and columns as years. Column names must be equal interval.}
 
 \item{f}{numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. Coincident with \code{U}.}
 
-\item{N}{numeric. A matrix of population with rows as ages and columns as years. Coincident with \code{U}.}
-
 \item{pi}{numeric. A matrix with distribution of childbearing with rows as ages and columns as years. Coincident with \code{U}.}
 
 \item{output_cohort}{integer. Year of birth of focal to return as output. Could be a vector. Should be within input data years range.}
@@ -34,11 +32,16 @@ kin_time_variant(
 \item{birth_female}{numeric. Female portion at birth.}
 
 \item{list_output}{logical. Results as a list with years elements (as a result of \code{output_cohort} and \code{output_period} combination), with a second list of \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
+
+\item{N}{numeric. A matrix of population with rows as ages and columns as years. Coincident with \code{U}.}
 }
 \value{
 A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives
 (for example \code{d} is daughter, \code{oa} is older aunts, etc.), living and death kin counts, and age of (living or time deceased) relatives. If \code{list_output = TRUE} then this is a list.
 }
 \description{
-Implementation of time variant Goodman-Keyfitz-Pullum equations based on Caswell (2021).
+Matrix implementation of time variant Goodman-Keyfitz-Pullum equations in a matrix framework.
+}
+\details{
+See Caswell (2021) for details on formulas.
 }
diff --git a/man/kin_time_variant_2sex.Rd b/man/kin_time_variant_2sex.Rd
new file mode 100644
index 0000000..bcb3ca9
--- /dev/null
+++ b/man/kin_time_variant_2sex.Rd
@@ -0,0 +1,65 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_time_variant_2sex.R
+\name{kin_time_variant_2sex}
+\alias{kin_time_variant_2sex}
+\title{Estimate kin counts in a time variant framework (dynamic rates) in a two-sex framework (Caswell, 2022)}
+\usage{
+kin_time_variant_2sex(
+  pf = NULL,
+  pm = NULL,
+  ff = NULL,
+  fm = NULL,
+  sex_focal = "f",
+  birth_female = 1/2.04,
+  pif = NULL,
+  pim = NULL,
+  nf = NULL,
+  nm = NULL,
+  output_cohort = NULL,
+  output_period = NULL,
+  output_kin = NULL,
+  list_output = FALSE
+)
+}
+\arguments{
+\item{pf}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+
+\item{pm}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+
+\item{ff}{numeric. Same as pf but for fertility rates.}
+
+\item{fm}{numeric. Same as pm but for fertility rates.}
+
+\item{sex_focal}{character. "f" for female or "m" for male.}
+
+\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring, this needs to be set as 1.}
+
+\item{pif}{numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default \code{NULL}.}
+
+\item{pim}{numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default \code{NULL}.}
+
+\item{nf}{numeric. Same as pf but for population distribution (counts or \verb{\%}). Optional.}
+
+\item{nm}{numeric. Same as pm but for population distribution (counts or \verb{\%}). Optional.}
+
+\item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.}
+
+\item{output_period}{integer. Vector of period years for returning results. Should be within input data years range.}
+
+\item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
+
+\item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Default \code{TRUE}.}
+
+\item{stable}{logic. Deprecated. Use \code{time_invariant}.}
+}
+\value{
+A data.frame with year, cohort, Focal´s age, related ages, sex and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), including living and dead kin at that age and sex.
+}
+\description{
+Two-sex matrix framework for kin count estimates with varying rates.
+This produces kin counts grouped by kin, age and sex of each relatives at each Focal´s age.
+For example, male cousins from aunts and uncles from different sibling's parents are grouped in one male count of cousins.
+}
+\details{
+See Caswell (2022) for details on formulas.
+}
diff --git a/man/output_period_cohort_combination.Rd b/man/output_period_cohort_combination.Rd
index 5b20baf..e53a40c 100644
--- a/man/output_period_cohort_combination.Rd
+++ b/man/output_period_cohort_combination.Rd
@@ -1,9 +1,16 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_time_variant.R
+% Please edit documentation in R/kin_time_variant.R, R/kin_time_variant_2sex.R
 \name{output_period_cohort_combination}
 \alias{output_period_cohort_combination}
 \title{defince apc combination to return}
 \usage{
+output_period_cohort_combination(
+  output_cohort = NULL,
+  output_period = NULL,
+  age = NULL,
+  years_data = NULL
+)
+
 output_period_cohort_combination(
   output_cohort = NULL,
   output_period = NULL,
@@ -12,5 +19,7 @@ output_period_cohort_combination(
 )
 }
 \description{
+defince apc to return.
+
 defince apc to return.
 }
diff --git a/man/timevarying_kin_2sex.Rd b/man/timevarying_kin_2sex.Rd
new file mode 100644
index 0000000..e2ad4ac
--- /dev/null
+++ b/man/timevarying_kin_2sex.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_time_variant_2sex.R
+\name{timevarying_kin_2sex}
+\alias{timevarying_kin_2sex}
+\title{one time projection kin}
+\usage{
+timevarying_kin_2sex(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
+}
+\arguments{
+\item{Ut}{numeric. A matrix of survival probabilities (or ratios).}
+
+\item{pit}{numeric. A matrix with distribution of childbearing.}
+
+\item{ages}{numeric.}
+
+\item{pkin}{numeric. A list with kin count distribution in previous year.}
+
+\item{ft}{numeric. A matrix of age-specific fertility rates.}
+}
+\description{
+one time projection kin. internal function.
+}
diff --git a/vignettes/Reference.Rmd b/vignettes/Reference.Rmd
index 33560b2..cd1bee3 100644
--- a/vignettes/Reference.Rmd
+++ b/vignettes/Reference.Rmd
@@ -5,7 +5,7 @@ output:
     toc: true
     toc_depth: 1
 vignette: >
-  %\VignetteIndexEntry{Use}
+  %\VignetteIndexEntry{Reference}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -21,10 +21,11 @@ Here, we'll show how `DemoKin` can be used to compute the number and age distrib
 
 First, we compute kin counts in a **time-invariant** framework. We assume that Focal and all of her relatives experience the 2015 mortality and fertility rates throughout their entire lives (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example: age-by-year matrices of survival probabilities (*swe_px*), survival ratios (*swe_Sx*), fertility rates (*swe_asfr*), and population numbers (*swe_pop*). You can see the data contained in `DemoKin` with `data(package="DemoKin")`. This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/) (see `?DemoKin::get_HMDHFD`). 
 
-In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of sruvival ratios and another vector of fertility rates. In this example, we get the data for the year 2015, and run the matrix models:
+In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of survival ratios and another vector of fertility rates. In this example, we get the data for the year 2015, and run the matrix models:
 
 ```{r, message=FALSE, warning=FALSE}
-library(DemoKin)
+library(devtools)
+load_all()
 library(tidyr)
 library(dplyr)
 library(ggplot2)
@@ -33,7 +34,7 @@ library(knitr)
 swe_surv_2015 <- swe_px[,"2015"]
 swe_asfr_2015 <- swe_asfr[,"2015"]
 # Run kinship models
-swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
+swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
 ```
 
 ### 1.1. Value
@@ -135,9 +136,9 @@ Let's take a look at the resulting kin counts for a Focal born in 1960, limiting
 ```{r, fig.height=6, fig.width=8}
 swe_time_varying <- 
   kin(
-    U = swe_px,
+    p = swe_px,
     f = swe_asfr,
-    N = swe_pop,
+    n = swe_pop,
     time_invariant =FALSE,
     output_cohort = 1960,
     output_kin = c("d","gd","ggd","m","gm","ggm")
@@ -240,7 +241,7 @@ demokin_svk1980_caswell2020 <-
     )
 ```
 
-As an example, consider the age-parity distribution of aunts, when Focal is 20 and 60 yo (this is equivalent to Figure 4 in Caswell [2021]). 
+Note that the function ask for risks already in a certain matrix format. As an example, consider the age-parity distribution of aunts, when Focal is 20 and 60 yo (this is equivalent to Figure 4 in Caswell [2021]). 
 
 ```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
 demokin_svk1980_caswell2020 %>% 
diff --git a/vignettes/TwoSex.Rmd b/vignettes/TwoSex.Rmd
index 3245293..01ed3c0 100644
--- a/vignettes/TwoSex.Rmd
+++ b/vignettes/TwoSex.Rmd
@@ -1,11 +1,11 @@
 ---
-title: "TwoExpected kin counts by type of relative: A matrix implementation"
+title: "Two-Sex expected kin counts by type of relative: A matrix implementation"
 output: 
   html_document:
     toc: true
     toc_depth: 1
 vignette: >
-  %\VignetteIndexEntry{Use}
+  %\VignetteIndexEntry{TwoSex}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -17,12 +17,13 @@ knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 Age distribution of Focal´s father when she born depends on male fertility pattern. Living siblings depends on sex composition (brothers and sisters) due to differential mortality risk. Intensity in care tasks is not the same between sex in many societies, so the sex of ego and his/her "sandwichness" change, because an average family network expects different roles in supporting. For these reasons, and many others, sex specific kin count estimates are important. Here we implement relations in Caswell (2022), not focusing in applications that can be analogous to the one-sex model, but in the specific advantages.
 
 ```{r, message=FALSE, warning=FALSE}
-library(DemoKin)
+# library(DemoKin)
+library(devtools)
+load_all()
 library(tidyr)
 library(dplyr)
 library(ggplot2)
 library(knitr)
-devtools::load_all()
 ```
 
 ### 1.1. Rates by sex
@@ -30,8 +31,8 @@ devtools::load_all()
 Female fertility by age is not a widespread available data source. Caswell (2022) takes Schoumaker (2019) makes available estimates for 160 countries, reporting  that male TFR almost always exceeds female TFR. We take the case of France in 2012 for showing how functions works (fertility and mortality data are available with the package, with column-sex values). Let´s see main differences in age distribution (TFR of 1.98 and 1.99 for males and females, practically the same)
 
 ```{r}
-fra_fert_f <- fra_fert_sex[,"ff"]
-fra_fert_m <- fra_fert_sex[,"fm"]
+fra_fert_f <- fra_asfr_sex[,"ff"]
+fra_fert_m <- fra_asfr_sex[,"fm"]
 fra_surv_f <- fra_surv_sex[,"pf"]
 fra_surv_m <- fra_surv_sex[,"pm"]
 sum(fra_fert_m)-sum(fra_fert_f)
@@ -59,9 +60,9 @@ kin_out <- kin_out$kin_summary %>%
                          T ~ kin)) %>%
   filter(kin %in% c("d", "m", "gm", "ggm", "s", "a"))
 kin_out %>% 
-  group_by(kin, age_focal, sex) %>%
+  group_by(kin, age_focal, sex_kin) %>%
   summarise(count=sum(count_living)) %>%
-  ggplot(aes(age_focal, count, fill=sex))+
+  ggplot(aes(age_focal, count, fill=sex_kin))+
   geom_area()+
   theme_bw() +
   facet_wrap(~kin)
@@ -72,7 +73,7 @@ Kin availability by sex allows to inspect its distribution, a traditional measur
 ```{r}
 kin_out %>% 
   group_by(kin, age_focal) %>%
-  summarise(sex_ratio=sum(count_living[sex=="m"], na.rm=T)/sum(count_living[sex=="f"], na.rm=T)) %>%
+  summarise(sex_ratio=sum(count_living[sex_kin=="m"], na.rm=T)/sum(count_living[sex_kin=="f"], na.rm=T)) %>%
   ggplot(aes(age_focal, sex_ratio))+
   geom_line()+
   theme_bw() +
@@ -84,9 +85,9 @@ How ego experiences relative deaths depends mainly on how wide is the sex-gap in
 ```{r}
 # sex ratio
 kin_out %>%
-  group_by(kin, sex, age_focal) %>%
+  group_by(kin, sex_kin, age_focal) %>%
   summarise(count=sum(count_dead)) %>%
-  ggplot(aes(age_focal, count, col=sex))+
+  ggplot(aes(age_focal, count, col=sex_kin))+
   geom_line()+
   theme_bw() +
   facet_wrap(~kin)
@@ -102,13 +103,13 @@ kin_out_androgynous <- kin2sex(fra_surv_f, fra_surv_f, fra_fert_f, fra_fert_f, s
 bind_rows(
   kin_out$kin_summary %>% mutate(type = "full"),
   kin_out_androgynous$kin_summary %>% mutate(type = "androgynous")) %>%
-  group_by(kin, age_focal, sex, type) %>%
+  group_by(kin, age_focal, sex_kin, type) %>%
   summarise(count = sum(count_living)) %>%
   ggplot(aes(age_focal, count, linetype = type)) +
   geom_line() +
   theme_bw() +
   theme(legend.position = "bottom", axis.text.x = element_blank()) +
-  facet_grid(row = vars(sex), col = vars(kin), scales = "free")
+  facet_grid(row = vars(sex_kin), col = vars(kin), scales = "free")
 ```
 
 Now we can multiply results from 1-sex model by the GKP factors by kin, to obtain a simple but very consistent approximation of totals (both sex) at different ages of Focal.  
@@ -189,11 +190,11 @@ kin_out_time_variant$kin_summary %>%
                          kin %in% c("ya", "oa") ~ "a",
                          T ~ kin)) %>%
   filter(kin %in% c("d", "m", "gm", "ggm", "s", "a")) %>%
-  group_by(type, kin, age_focal, sex) %>%
+  group_by(type, kin, age_focal, sex_kin) %>%
   summarise(count=sum(count_living)) %>%
   ggplot(aes(age_focal, count, linetype=type))+
   geom_line()+ theme_bw() +
-  facet_grid(cols = vars(kin), rows=vars(sex), scales = "free")
+  facet_grid(cols = vars(kin), rows=vars(sex_kin), scales = "free")
 ```
 
  

From 9deb29bbb7029d465858b9d11397e38cbadce8ce Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Sat, 4 Feb 2023 10:21:51 -0300
Subject: [PATCH 08/89] pre load cran

---
 DESCRIPTION                                   |   7 +-
 NAMESPACE                                     |   1 -
 R/kin.R                                       |   1 +
 R/kin2sex.R                                   |  11 +-
 R/kin_time_variant.R                          |   2 +-
 R/kin_time_variant_2sex.R                     |  11 +-
 README.Rmd                                    |  23 +-
 README.md                                     |  28 +-
 dev/PENDS.txt                                 |   7 -
 vignettes/TwoSex.Rmd => dev/TwoSex_mine.Rmd   |   0
 dev/calling_kinship_SVK_4867.m                |  86 ----
 {R => dev}/get_HMDHFD.R                       |   0
 dev/kinship_function_parity_4867.m            | 199 --------
 dev/matrix_construction_4867.m                | 102 ----
 dev/readme.txt                                |   0
 dev/tests/repl_caswell.R                      | 443 ------------------
 dev/tests/repl_caswell_first_year.R           | 106 -----
 dev/tests/timevarying_kin.m                   | 180 -------
 .../figures/DemoKin-Logo.png                  | Bin
 man/get_HMDHFD.Rd                             |  41 --
 man/kin.Rd                                    |   2 +
 man/kin2sex.Rd                                |  12 +-
 man/kin_time_variant.Rd                       |   4 +-
 man/kin_time_variant_2sex.Rd                  |   4 +-
 man/output_period_cohort_combination.Rd       |   2 +-
 man/timevarying_kin_2sex.Rd                   |   9 +-
 .../{Reference.Rmd => Reference_OneSex.Rmd}   |  10 +-
 vignettes/Reference_TwoSex.Rmd                | 271 +++++++++++
 28 files changed, 334 insertions(+), 1228 deletions(-)
 delete mode 100644 dev/PENDS.txt
 rename vignettes/TwoSex.Rmd => dev/TwoSex_mine.Rmd (100%)
 delete mode 100644 dev/calling_kinship_SVK_4867.m
 rename {R => dev}/get_HMDHFD.R (100%)
 delete mode 100644 dev/kinship_function_parity_4867.m
 delete mode 100644 dev/matrix_construction_4867.m
 delete mode 100644 dev/readme.txt
 delete mode 100644 dev/tests/repl_caswell.R
 delete mode 100644 dev/tests/repl_caswell_first_year.R
 delete mode 100644 dev/tests/timevarying_kin.m
 rename DemoKin-Logo.png => man/figures/DemoKin-Logo.png (100%)
 delete mode 100644 man/get_HMDHFD.Rd
 rename vignettes/{Reference.Rmd => Reference_OneSex.Rmd} (98%)
 create mode 100644 vignettes/Reference_TwoSex.Rmd

diff --git a/DESCRIPTION b/DESCRIPTION
index 2e5402c..4834c36 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,8 @@
 Package: DemoKin
-Title: Demokin
-Description: Estimate population kin counts and its distribution by type and age
+Title: Estimate population kin counts.
+Description: Estimate population kin counts and its distribution by type, age and sex.
+    The package implements one-sex and two-sex framework for studying living-death availability,
+    with time varying rates or not, and multi-stage model.
 Version: 1.0.0
 Authors@R: c(
     person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"),
@@ -31,6 +33,7 @@ Imports:
     magrittr,
     data.table,
     lifecycle
+URL: https://github.com/IvanWilli/DemoKin
 BugReports: https://github.com/IvanWilli/DemoKin/issues
 Depends: 
     R (>= 2.10)
diff --git a/NAMESPACE b/NAMESPACE
index 5502931..2d84ee7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,7 +2,6 @@
 
 export("%>%")
 export(demokin_codes)
-export(get_HMDHFD)
 export(kin)
 export(kin2sex)
 export(kin_multi_stage)
diff --git a/R/kin.R b/R/kin.R
index 866f1c9..cdc27d7 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -15,6 +15,7 @@
 #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring,
 #' this needs to be set as 1.
 #' @param stable logic. Deprecated. Use `time_invariant`.
+#' @param U logic. Deprecated. Use `p`.
 #' @return A list with:
 #' \itemize{
 #'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter,
diff --git a/R/kin2sex.R b/R/kin2sex.R
index aa46eba..a9b2aa3 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -2,10 +2,11 @@
 
 #' @description Implementation of two-sex matrix kinship model. This produces kin counts grouped by kin, age and sex of
 #' each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
-#' are grouped in one male count of cousins.
+#' are grouped in one male count of cousins. Note that the output labels relative following female notation: the label `m`
+#' refers to either mothers or fathers, and column `sex_kin` determine the sex of the relative.
 #' @details See Caswell (2022) for details on formulas.
-#' @param pf numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
-#' @param pm numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param pf numeric. A vector (atomic) or  matrix with female probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param pm numeric. A vector (atomic) or  matrix with male probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
 #' @param ff numeric. Same as pf but for fertility rates.
 #' @param fm numeric. Same as pm but for fertility rates.
 #' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
@@ -18,10 +19,10 @@
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
 #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
-#' @param stable logic. Deprecated. Use `time_invariant`.
 #' @return A list with:
 #' \itemize{
-#'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age.}
+#'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` could be daughter or son depending `sex_kin`,
+#'  `oa` is older aunts or uncles also depending `sex_kin` value, etc.), including living and dead kin at that age.}
 #'  \item{kin_summary}{ a data frame with Focal´s age, related ages, sex and type of kin, with indicators obtained processing `kin_full`, grouping by cohort or period (depending on the given arguments):}
 #'  {\itemize{
 #'  \item{`count_living`}{: count of living kin at actual age of Focal}
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index c73f1ec..f9e9383 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -4,7 +4,7 @@
 #' @details See Caswell (2021) for details on formulas.
 #' @param p numeric. A matrix of survival ratios with rows as ages and columns as years. Column names must be equal interval.
 #' @param f numeric. A matrix of age-specific fertility rates with rows as ages and columns as years. Coincident with `U`.
-#' @param N numeric. A matrix of population with rows as ages and columns as years. Coincident with `U`.
+#' @param n numeric. A matrix of population with rows as ages and columns as years. Coincident with `U`.
 #' @param pi numeric. A matrix with distribution of childbearing with rows as ages and columns as years. Coincident with `U`.
 #' @param output_cohort integer. Year of birth of focal to return as output. Could be a vector. Should be within input data years range.
 #' @param output_period integer. Year for which to return kinship structure. Could be a vector. Should be within input data years range.
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index 0ffe306..89efe85 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -8,7 +8,6 @@
 #' @param pm numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
 #' @param ff numeric. Same as pf but for fertility rates.
 #' @param fm numeric. Same as pm but for fertility rates.
-#' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
 #' @param sex_focal character. "f" for female or "m" for male.
 #' @param pif numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default `NULL`.
 #' @param pim numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default `NULL`.
@@ -18,7 +17,7 @@
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
 #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
-#' @param stable logic. Deprecated. Use `time_invariant`.
+#' @param list_output logical. Results as a list with years elements (as a result of `output_cohort` and `output_period` combination), with a second list of `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE`
 #' @return A data.frame with year, cohort, Focal´s age, related ages, sex and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age and sex.
 #' @export
 
@@ -182,8 +181,10 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
 #' @description one time projection kin. internal function.
 #'
 #' @param Ut numeric. A matrix of survival probabilities (or ratios).
-#' @param ft numeric. A matrix of age-specific fertility rates.
+#' @param Ft numeric. A matrix of age-specific fertility rates.
+#' @param Ft_star numeric. Ft but for female fertility.
 #' @param pit numeric. A matrix with distribution of childbearing.
+#' sex_focal
 #' @param ages numeric.
 #' @param pkin numeric. A list with kin count distribution in previous year.
 #
@@ -243,9 +244,9 @@ timevarying_kin_2sex<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
   return(kin_list)
 }
 
-#' defince apc combination to return
+#' APC combination to return
 
-#' @description defince apc to return.
+#' @description define apc combination to return in `kin` and `kin2sex`.
 #'
 output_period_cohort_combination <- function(output_cohort = NULL, output_period = NULL, age = NULL, years_data = NULL){
 
diff --git a/README.Rmd b/README.Rmd
index 5eb0592..d2426f1 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -2,8 +2,6 @@
 output: github_document
 ---
 
-<!-- README.md is generated from README.Rmd. Please edit that file -->
-
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
@@ -23,12 +21,12 @@ library(knitr)
 ::: {.column width="60%"}
 
 
-`DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell (2019), Caswell (2020), and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974).
+`DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell (2019, 2020, 2022), and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974).
 
 :::
 ::: {.column width="40%"}
 
-<img src="DemoKin-Logo.png" align="right" width="200" />
+<img src="man/figures/DemoKin-Logo.png" align="right" width="200" />
 
 :::
 ::::::::::::::
@@ -52,14 +50,14 @@ We then ask:
 
 Let's explore this using the Swedish data already included with `DemoKin`.
 
-```{r, fig.height=6, fig.width=8}
+```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE}
 library(DemoKin)
 swe_surv_2015 <- swe_px[,"2015"]
 swe_asfr_2015 <- swe_asfr[,"2015"]
-swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
+swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
 ```
 
-*px* is the survival probability by age from a life table and *f* are the age specific fertility raties by age (see `?kin` for details). 
+*p* is the survival probability by age from a life table and *f* are the age specific fertility raties by age (see `?kin` for details). 
 
 Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or 'Keyfitz' kinship diagram with the function `plot_diagram`:
 
@@ -80,7 +78,7 @@ kable(DemoKin::demokin_codes()[-2])
 
 ## Vignette
 
-For more details, including an extension to time varying-populations rates, deceased kin, and multi-state models, see `vignette("Reference", package = "DemoKin")`.
+For more details, including an extension to time varying-populations rates, deceased kin, and multi-state models in a one-sex framework, see `vignette("Reference_OneSex", package = "DemoKin")`. For the case of two-sex see `vignette("Reference_TwoSex", package = "DemoKin")`.
 If the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
 
 ## Citation
@@ -105,11 +103,6 @@ Caswell, H. 2020. The formal demography of kinship II: Multistate models, parity
 
 Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III. Kinship Dynamics with Time-Varying Demographic Rates.” Demographic Research 45: 517–46. doi:10.4054/DemRes.2021.45.16.
 
-Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27. doi:10.1016/0040-5809(74)90049-5.
-
-<!-- ## Next steps: -->
-
-<!-- 1. Implement two-sex matrix kinship models -->
-<!-- 1. Improve documentation and vignette of package  -->
-
+Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359–396. 
 
+Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27. doi:10.1016/0040-5809(74)90049-5.
diff --git a/README.md b/README.md
index 0142d8b..a8badbf 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,4 @@
 
-<!-- README.md is generated from README.Rmd. Please edit that file -->
-
 # DemoKin
 
 <div class="columns">
@@ -10,14 +8,14 @@
 `DemoKin` uses matrix demographic methods to compute expected (average)
 kin counts from demographic rates under a range of scenarios and
 assumptions. The package is an R-language implementation of Caswell
-(2019), Caswell (2020), and Caswell and Song (2021). It draws on
-previous theoretical development by Goodman, Keyfitz and Pullum (1974).
+(2019, 2020, 2022), and Caswell and Song (2021). It draws on previous
+theoretical development by Goodman, Keyfitz and Pullum (1974).
 
 </div>
 
 <div class="column" width="40%">
 
-<img src="DemoKin-Logo.png" align="right" width="200" />
+<img src="man/figures/DemoKin-Logo.png" align="right" width="200" />
 
 </div>
 
@@ -50,11 +48,11 @@ Let’s explore this using the Swedish data already included with
 library(DemoKin)
 swe_surv_2015 <- swe_px[,"2015"]
 swe_asfr_2015 <- swe_asfr[,"2015"]
-swe_2015 <- kin(U = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
+swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
 ```
 
-*px* is the survival probability by age from a life table and *f* are
-the age specific fertility raties by age (see `?kin` for details).
+*p* is the survival probability by age from a life table and *f* are the
+age specific fertility raties by age (see `?kin` for details).
 
 Now, we can visualize the implied kin counts (i.e., the average number
 of living kin) of Focal at age 35 using a network or ‘Keyfitz’ kinship
@@ -93,9 +91,10 @@ Relatives are identified by a unique code:
 ## Vignette
 
 For more details, including an extension to time varying-populations
-rates, deceased kin, and multi-state models, see
-`vignette("Reference", package = "DemoKin")`. If the vignette does not
-load, you may need to install the package as
+rates, deceased kin, and multi-state models in a one-sex framework, see
+`vignette("Reference_OneSex", package = "DemoKin")`. For the case of
+two-sex see `vignette("Reference_TwoSex", package = "DemoKin")`. If the
+vignette does not load, you may need to install the package as
 `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
 
 ## Citation
@@ -134,10 +133,9 @@ Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III.
 Kinship Dynamics with Time-Varying Demographic Rates.” Demographic
 Research 45: 517–46. <doi:10.4054/DemRes.2021.45.16>.
 
+Caswell, H. (2022). The formal demography of kinship IV: Two-sex models
+and their approximations. Demographic Research, 47, 359–396.
+
 Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation
 and the frequency of various kinship relationships. Theoretical
 Population Biology 5(1):1–27. <doi:10.1016/0040-5809(74)90049-5>.
-
-<!-- ## Next steps: -->
-<!-- 1. Implement two-sex matrix kinship models -->
-<!-- 1. Improve documentation and vignette of package  -->
diff --git a/dev/PENDS.txt b/dev/PENDS.txt
deleted file mode 100644
index ecbf143..0000000
--- a/dev/PENDS.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-1) Set no specific argument for Pb: in the case the user wants to use it, that can be included by her/himself in the F matrix, implicitly. - ok
-1.1) caswell´s assumption stable: ft[1,1:ages] = f * U * birth_female
-2) Include a paragraph in the "using" vignette to show this option.
-3) Non-stable without pi or N as argument: give user an output anyways and a message "A stable assumption was used for the age distribution of the mother in each input year".
-4) Replicate Hal´s output for dinamycs.
-5) Correct the appendix: survival/probabilities.
-6) Finish Multi-stage.
\ No newline at end of file
diff --git a/vignettes/TwoSex.Rmd b/dev/TwoSex_mine.Rmd
similarity index 100%
rename from vignettes/TwoSex.Rmd
rename to dev/TwoSex_mine.Rmd
diff --git a/dev/calling_kinship_SVK_4867.m b/dev/calling_kinship_SVK_4867.m
deleted file mode 100644
index f51c641..0000000
--- a/dev/calling_kinship_SVK_4867.m
+++ /dev/null
@@ -1,86 +0,0 @@
-%script to calculate kinship results
-%this script calls the function kinship_function_parity_4867
-%requires the function vecperm.m to create vec-permutation matrix
-%
-% Supplement to:
-% Caswell, H. 2020. The formal demography of kinship II. Multistate models,
-% parity, and sibship. Demographic Research 42:1097-1144
-%
-% Has been successfully used under Matlab R2018b
-
-%specify range of years to analyze
-years=1960:2014;
-
-%years=2002;
-
-numyears=length(years);  %specific to SVK data
-%add path to location of matrices
-addpath('SVK_kinmats/')
-
-for iy=1:numyears
-    year=years(iy)
-    
-    %specify name of matrix file
-    fname=char(['SVKmats' num2str(1950+iy-1) '.mat']);
-    %load matrix file
-    load(fname)
-    
-    %create the block diagonal matrices
-    
-    %identity matrices that are useful
-    Iom=eye(om);
-    Is=eye(s);
-    
-    bbU=zeros(s*om);
-    bbF=zeros(s*om);
-    for i=1:om
-        bbU = bbU + kron(Iom(:,i)*Iom(i,:),U{i});
-        bbF = bbF + kron(Iom(:,i)*Iom(i,:),F{i});
-    end
-    bbD=zeros(s*om);
-    bbH=zeros(s*om);
-    for i=1:s
-        bbD = bbD+kron(Is(:,i)*Is(i,:),D{i});
-        bbH = bbH+kron(Is(:,i)*Is(i,:),H{i});
-    end
-    
-    %create the age-stage matrices using the vec permuation formula
-    K=vecperm(s,om);
-    Ut= K'*bbD*K*bbU;
-    Ft= K'*bbH*K*bbF;
-    
-    %conditional transition matrix, conditional on survival
-    Gt=Ut*pinv(diag(sum(Ut)));
-    
-    %calculate distributions of mothers
-    %projection matrix Atilde
-    At=Ut+Ft;
-    %eigenvalues and right eigenvectors
-    [wt,d]=eig(At);
-    d=diag(d);
-    %find maximum eigenvalue
-    pick=find(d==max(d));
-    wt=wt(:,pick);
-    %stable age-parity distribution normalized to sum to 1
-    wt=wt/sum(wt);
-    lambda=d(pick)
-    
-    %age-stage distribution of mothers
-    pit=Ft(1,:)'.*wt;
-    pit=pit/sum(pit);
-    %marginal age distribution of mothers
-    piage=kron(Iom,ones(s,1)')*pit;
-    
-    clear At
-    
-    %add path to call the kinship program
-    path('../',path)
-    
-    %call the kinship function
-    kinout=kinship_function_parity(Ut,Ft,Gt,wt,pit,piage);
-    
-    %save the kin output
-    %include path to output folder
-    myname=char(['SVK_kinout/SVKkinout' num2str(years(iy)) '.mat'])
-    save(myname,'kinout')
-end
diff --git a/R/get_HMDHFD.R b/dev/get_HMDHFD.R
similarity index 100%
rename from R/get_HMDHFD.R
rename to dev/get_HMDHFD.R
diff --git a/dev/kinship_function_parity_4867.m b/dev/kinship_function_parity_4867.m
deleted file mode 100644
index 897bcdb..0000000
--- a/dev/kinship_function_parity_4867.m
+++ /dev/null
@@ -1,199 +0,0 @@
-function out=kinship_function_parity(Ut,Ft,Gt,wt,pit,piage)
-%
-%function to compute kinship network for multistate age x parity model
-% Supplement to:
-% Caswell, H. 2020. The formal demography of kinship II. Multistate models,
-% parity, and sibship. Demographic Research 42:1097-1144
-%
-% Has been successfully used under Matlab R2018b
-%
-%
-%inputs
-% Ut=age-stage transition matrix
-% Ft = age-stage fertility matrix
-% Gt=age-stage transition matrix conditional on survival
-% wt=stable age-stage distribution, normalized to sum to 1
-% pit=age-stage distribution of mothers
-% piage = marginal age distribution of mothers
-
-
-%number of age classes
-om=length(piage);
-%number of stages
-s=length(pit)/om;
-
-%identity matrices useful in calculations
-Iom=eye(om);
-Is=eye(s);
-Isom=eye(s*om);
-
-%frequently used zero vector for initial condition
-zvec=zeros(s*om,1);
-
-%frequently used om-1 limit for iterations
-omz=om-1;
-
-% the following code calculates age-stage distributions, 
-% for each type of kin, for each age x of Focal,
-% and stores these as columns of an array
-% e.g., a(x) = daughters at age x; A(:,x) contains a(x)
-
-% dynamics of Focal
-% initial condition
-phiz=zeros(s*om,1);
-phiz(1)=1;
-%age-stage vector of Focal, conditional on survival
-Phi(:,1)=phiz;
-for ix=1:omz
-    Phi(:,ix+1)=Gt*Phi(:,ix);
-end
-
-% a: daughters of focal
-
-az=zvec;
-A(:,1)=az;
-for ix=1:omz
-    A(:,ix+1)=Ut*A(:,ix) + Ft*Phi(:,ix);
-end % for ix
-
-
-% b = granddaughters of Focal
-b=zvec;
-B(:,1)=b;
-for ix=1:omz
-    B(:,ix+1)=Ut*B(:,ix) + Ft*A(:,ix);
-end
-
-
-% c = greatgranddaughters of Focal
-c=zvec;
-C(:,1)=c;
-for ix=1:omz
-    C(:,ix+1)=Ut*C(:,ix) +Ft*B(:,ix);
-end
-
-
-% d = mothers of Focal
-% conditional on mother having parity >0 
-
-%momarray is an array with pit in each column
-momarray=pit*ones(1,om);
-
-Z=eye(s);
-Z(1,1)=0;
-for imom=1:om  %go through all columns of momarray
-    E=Iom(:,imom)*Iom(imom,:);
-    momarray(:,imom)=kron(E,Z)*momarray(:,imom);
-    %selects age imom, and eliminates the zero parity row of momarray
-        
-end
-%rescale columns of momarray to sum to 1
-momarray=momarray*pinv(diag(sum(momarray)));
-
-%set dzero to the average of the momarray over the ages of moms at birth of
-%children
-dzero=momarray*piage;
-
-D(:,1)=dzero;
-for ix=1:omz
-    D(:,ix+1)=Ut*D(:,ix);
-end
-
-
-% g = maternal grandmothers of Focal
-gzero=D*piage;
-
-G(:,1)=gzero;
-for ix=1:omz
-    G(:,ix+1)=Ut*G(:,ix);
-end
-
-
-% h = great-grandmothers of Focal
-hzero=G*piage;
-H(:,1)=hzero;
-for ix=1:omz
-    H(:,ix+1)=Ut*H(:,ix) + 0;
-end
-
-% m = older sisters of Focal
-mzero=A*piage;
-M(:,1)=mzero;
-for ix=1:omz
-    M(:,ix+1)=Ut*M(:,ix) + 0;
-end
-
-% n = younger sisters of Focal
-nzero=zvec;
-N(:,1)=nzero;
-for ix=1:omz
-    N(:,ix+1)=Ut*N(:,ix) + Ft*D(:,ix);
-end
-
-
-% p = nieces through older sisters of Focal
-pzero=B*piage;
-P(:,1)=pzero;
-for ix=1:omz
-    P(:,ix+1)=Ut*P(:,ix) + Ft*M(:,ix);
-end
-
-% q = nieces through younger sisters of Focal
-qzero=zvec;
-Q(:,1)=qzero;
-for ix=1:omz
-    Q(:,ix+1)=Ut*Q(:,ix) + Ft*N(:,ix);
-end
-
-% r = aunts older than mother of Focal
-rzero=M*piage;
-R(:,1)=rzero;
-for ix=1:omz
-    R(:,ix+1)=Ut*R(:,ix) + 0;
-end
-
-% s = aunts younger than mother of Focal
-szero=N*piage;
-S(:,1)=szero;
-for ix=1:omz
-    S(:,ix+1)=Ut*S(:,ix) + Ft*G(:,ix);
-end
-
-% t = cousins from aunts older than mother of Focal
-tzero=P*piage;
-T(:,1)=tzero;
-for ix=1:omz
-    T(:,ix+1)=Ut*T(:,ix) + Ft*R(:,ix);
-end
-
-
-% v = cousins from aunts younger than mother of Focal
-vzero=Q*piage;
-V(:,1)=vzero;
-for ix=1:omz
-    V(:,ix+1)=Ut*V(:,ix) + Ft*S(:,ix);
-end %for i
-
-
-%overall kinship matrices, concatenating all kin
-allkin=cat(3,A,B,C,D,G,H,M,N,P,Q,R,S,T,V);
-
-%combining older and younger categories
-% for sisters, neices, aunts, and cousins
-allkin2=cat(3,A,B,C,D,G,H,M+N,P+Q,R+S,T+V);
-
-%output structure
-out.allkin=allkin;
-out.allkin2=allkin2;
-out.Phi=Phi;
-out.pit=pit;
-out.piage=piage;
-out.om=om;
-out.s=s;
- out.Ut=Ut;
-out.Ft=Ft;
-out.Gt=Gt;
-
-
-
-
diff --git a/dev/matrix_construction_4867.m b/dev/matrix_construction_4867.m
deleted file mode 100644
index 1f6c75e..0000000
--- a/dev/matrix_construction_4867.m
+++ /dev/null
@@ -1,102 +0,0 @@
-
-% script to prepare matrices for multistate age x parity model
-% Supplement to:
-% Caswell, H. 2020. The formal demography of kinship II. Multistate models,
-% parity, and sibship. Demographic Research 42:1097-1144
-%
-%requires Matlab Table files obtained from HMD (fltper) and HFD (mi)
-% Has been successfully used under Matlab R2018b
-
-%add folder contraining the table files to path
-addpath('SVK_tables/')
-
-% load the female lifetable file
-load('SVKfltperTable.mat')
-%columns in this Table: Year,Age,mx,qx,ax,lx,dx,Lx,Tx,ex
-lt=ltable;
-
-%load the parity state transition file
-load('SVKmiTable.mat')
-%columns: Year,Age,mi1,mi2,mi3,mi4,mi5p
-
-%find year ranges
-minfertyear=min(fert.Year);
-maxfertyear=max(fert.Year);
-
-minltyear=min(lt.Year);
-maxltyear=max(lt.Year);
-
-%pick a starting year and ending year
-startyear=max([minfertyear minltyear]);
-endyear=min([maxfertyear maxltyear]);
-
-%array of years and number of years
-years=startyear:endyear;
-numyears=endyear-startyear+1;
-
-for iy=1:numyears
-    years(iy);
-    
-    %find life table and qx array for year iy
-    pick=find(lt.Year==years(iy));
-    qx=table2array(lt(pick,4));
-    
-    %find fertility and create fertility array
-    pick=find(fert.Year==years(iy));
-    fertarray=table2array(fert(pick,[2:7]));
-    
-    %number of age classes
-    %om=length(qx)-1;
-    om=length(qx)-1;
-    %number of parity classes
-    s=6;
-    
-    %extend the fertility array
-    startfert=fertarray(1,1);
-    endfert=fertarray(end,1);
-    %put zeros before age of first reproduction
-    fertarray=[zeros(startfert-1,6); fertarray];
-    fertarray(1:startfert-1,1)=(1:startfert-1)';
-    %put zeros after age of last reproduction
-    fertarray=[fertarray; zeros(om-endfert,6)];
-    fertarray(endfert+1:om,1)=(endfert+1:om)';
-    
-    %remove age column from fertarray
-    fertarray=fertarray(:,2:6);
-    
-    %construct the stage transition matrices using probabilities
-    for i=1:om
-        U{i} = diag(fertarray(i,:),-1);
-        %transform subdiagonals to probabilities
-        U{i}=U{i}./(1+0.5*U{i});
-        %fill in diagonal entries
-        U{i}=U{i}+diag([1-diag(U{i},-1) ; 1]);
-    end
-    
-    %construct the age transition and survival matrices
-    for i=1:s
-        D{i}=diag(1-qx(1:om-1),-1);
-    end
-    
-    %construct fertility matrices
-    for i=1:om
-        F{i}=zeros(s,s);
-        F{i}(1,1:s-1)=diag(U{i},-1);
-        F{i}(1,s)=U{i}(s,s-1);
-        %divide fertility by 2
-        F{i}=F{i}/2;
-    end
-    
-    %stage assignment matrices
-    for i=1:s
-        H{i}=zeros(om,om);
-        H{i}(1,:)=1;
-    end
-    
-    %include path to folder where matrix files are to be stored
-    myname=char(['SVK_kinmats/SVKmats' num2str(years(iy)) '.mat'])
-    %save the matrices into a .mat file
-    save(myname,'U','D','F','H','om','s')
-    
-end
-
diff --git a/dev/readme.txt b/dev/readme.txt
deleted file mode 100644
index e69de29..0000000
diff --git a/dev/tests/repl_caswell.R b/dev/tests/repl_caswell.R
deleted file mode 100644
index b81c5ee..0000000
--- a/dev/tests/repl_caswell.R
+++ /dev/null
@@ -1,443 +0,0 @@
-# replicating Caswell´s figures: choose some kin
-
-library(devtools)
-load_all()
-library(DemoKin)
-library(tidyverse)
-library(progress)
-library(R.matlab)
-load("tests/test.RData")
-
-# basic
-debugonce(kin_time_variant)
-swe_kin_period_pack <- kin(U = swe_surv,
-                      f = swe_asfr,
-                      N = swe_pop,
-                      time_invariant = F,
-                      birth_female = 1,
-                      output_period = c(1900, 1950, 2010),
-                      output_kin = c("d","gd","m","gm","oa", "os"))
-
-swe_kin_period_pack$kin_full %>%
-  filter(alive == "yes") %>%
-  group_by(age_focal, kin, year) %>%
-  summarise(count = sum(count, na.rm=T)) %>%
-  ggplot(aes(age_focal, count, color=factor(year))) +
-  geom_line() +
-  facet_wrap(~kin, scales="free_y")
-
-# time variant ------------------------------------------------------------
-
-# inputs
-input_time_variant <- readMat("tests/SWEhist_matrices.mat")
-input_time_variant_proj <- readMat("tests/SWEproj_matrices.mat")
-# class(input_time_variant)
-# names(input_time_variant)
-# length(input_time_variant[["matrices"]]) # number of years
-# input_time_variant[["matrices"]][[128]][[1]][[1]] # U
-# input_time_variant[["matrices"]][[1]][[1]][[2]] # F
-# input_time_variant[["matrices"]][[1]][[1]][[3]] # popsize
-# input_time_variant[["matrices"]][[1]][[1]][[4]] # pi
-# length(input_time_variant_proj[["matrices"]]) # number of years
-
-U_hal <- f_hal <-N_hal <- pi_hal <-matrix(rep(0,111))
-for(y in 1:128){
-  # y = 1
-  U <- input_time_variant[["matrices"]][[y]][[1]][[1]] %>% as.matrix()
-  f <- input_time_variant[["matrices"]][[y]][[1]][[2]] %>% as.matrix()
-  N <- input_time_variant[["matrices"]][[y]][[1]][[3]] %>% as.matrix()
-  pi <- input_time_variant[["matrices"]][[y]][[1]][[4]] %>% as.matrix()
-  U_hal <- cbind(U_hal, c(U[col(U)==row(U)-1], U[ncol(U),nrow(U)]))
-  f_hal <- cbind(f_hal ,f[1,])
-  N_hal <- cbind(N_hal ,N)
-  pi_hal <-cbind(pi_hal, pi)
-}
-U_hal_end <- U_hal[,-1]
-f_hal_end <- f_hal[,-1]
-N_hal_end <- N_hal[,-1]
-pi_hal_end <-pi_hal[,-1]
-colnames(U_hal_end) <- colnames(f_hal_end) <- colnames(N_hal_end) <- colnames(pi_hal_end) <-1891:2018
-dim(U_hal_end);class(U_hal_end %>% as.matrix)
-
-# period
-swe_kin_period <- kin(U = U_hal_end %>% as.matrix(),
-                      f = f_hal_end %>% as.matrix(),
-                      pi = pi_hal_end %>% as.matrix(),
-                      time_invariant = F,
-                      birth_female = 1,
-                      output_period = c(1891,1921,1951,2010),
-                      output_kin = c("d","gd","m","gm","oa", "os"))
-
-# check first-row plots from figures 5-A  and 5-B from https://www.demographic-research.org/volumes/vol45/16/45-16.pdf
-swe_kin_period$kin_full %>%
-  filter(alive == "yes") %>%
-  group_by(age_focal, kin, year) %>%
-  summarise(count = sum(count, na.rm=T)) %>%
-  ggplot(aes(age_focal, count, color=factor(year))) +
-  geom_line() +
-  facet_wrap(~kin, scales="free_y")
-
-# read from https://www.dropbox.com/t/3YiILmn7SpczN3oM
-output_time_variant <- readMat("tests/time-varying_sweden.mat")
-
-# inspect the way the package reads
-# class(output_time_variant)
-# names(output_time_variant)
-# length(output_time_variant[["allkin"]]) # number of years
-# length(output_time_variant[["allkin"]][[1]])
-# length(output_time_variant[["allkin"]][[1]])
-# class(output_time_variant[["allkin"]][[1]][[1]]) # 1 array with kin matrix
-# dim(output_time_variant[["allkin"]][[1]][[1]][,,14]) # the matrix of the nth kin, 111 ages
-
-# use own codes to interpret
-codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
-caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n")
-
-# re arrange all data to a dataframe
-output_time_variant_df <- map_df(1:128, function(i){
-  array_branch(output_time_variant[["allkin"]][[i]][[1]], margin = 3) %>%
-    map_df(., as.data.frame)}) %>%
-  setNames(as.character(0:110)) %>%
-  bind_cols(crossing(year = 1891+(0:127),
-                     kin_index = 1:14,
-                     age_kin = 0:110)) %>%
-  inner_join(tibble(kin = codes, caswell_codes) %>%
-               arrange(caswell_codes) %>% mutate(kin_index = 1:14))
-
-# check dimension: 128 years, 14 types of kin, 111 ages
-nrow(output_time_variant_df); 128*14*111
-
-# check first-row plots from figures 5-A  and 5-B from https://www.demographic-research.org/volumes/vol45/16/45-16.pdf
-output_time_variant_df %>%
-  filter(year %in% c(1891,1921,1951,2010), kin %in% c("d","gd", "m", "gm", "oa", "os")) %>%
-  pivot_longer(`0`:`110`, names_to = "age", values_to = "count") %>%
-  mutate(age = as.integer(age)) %>%
-  group_by(age, kin, year) %>%
-  summarise(count = sum(count)) %>%
-  ggplot(aes(age, count, color=factor(year))) +
-  geom_line() +
-  facet_wrap(~kin, scales="free_y")
-
-# differences - look d, gd, in 1891 and 1951
-swe_period_together <- swe_kin_period$kin_full %>%
-  filter(alive == "yes") %>%
-  filter(year %in% c(1891,1921,1951,2010), kin %in% c("d","gd","m","gm","oa", "os")) %>%
-  group_by(age_focal, kin, year) %>% summarise(count_demokin = sum(count, na.rm=T)) %>%
-  inner_join(
-    output_time_variant_df %>%
-      filter(year %in% c(1891,1921,1951,2010), kin %in% c("d","gd", "m", "gm","oa", "os")) %>%
-      pivot_longer(`0`:`110`, names_to = "age", values_to = "count") %>%
-      mutate(age = as.integer(age)) %>%
-      group_by(age_focal=age, kin, year) %>%
-      summarise(count_paper = sum(count)))
-
-swe_period_together %>%
-  filter(year == 1891) %>%
-  ggplot() +
-  geom_line(aes(age_focal, count_demokin, color=factor(year)), linetype=1) +
-  geom_line(aes(age_focal, count_paper, color=factor(year)), linetype=2) +
-  facet_wrap(~kin, scales="free_y")
-
-swe_period_rel_dif <- swe_period_together %>%
-  mutate(rel_dif = round(100*(count_paper/count_demokin-1),3)) %>%
-  arrange(year, kin) %>%
-  as.data.frame() %>%
-  group_by(year, kin) %>% summarise(sum(rel_dif, na.rm=T))
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# to bind projected
-# U_hal <- U_hal[1:106,]
-# f_hal <- f_hal[1:106,]
-# N_hal <- N_hal[1:106,]
-# pi_hal <-pi_hal[1:106,]
-# for(y in 1:102){
-#   # y = 1
-#   U <- input_time_variant_proj[["matrices"]][[y]][[1]][[1]]
-#   f <- input_time_variant_proj[["matrices"]][[y]][[1]][[2]]
-#   N <- input_time_variant_proj[["matrices"]][[y]][[1]][[3]]
-#   pi <- input_time_variant_proj[["matrices"]][[y]][[1]][[4]]
-#   U_hal <- U_hal %>% bind_cols(c(U[col(U)==row(U)-1], U[ncol(U),nrow(U)]))
-#   f_hal <- f_hal %>% bind_cols(f[1,])
-#   N_hal <- N_hal %>% bind_cols(N)
-#   pi_hal <-pi_hal%>% bind_cols(as.numeric(pi))
-# }
-# dim(U_hal[,-1])
-# U_hal_end <- U_hal[,-1] %>% setNames(as.character(1891:2120))
-# f_hal_end <- f_hal[,-1] %>% setNames(as.character(1891:2120))
-# N_hal_end <- N_hal[,-1] %>% setNames(as.character(1891:2120))
-# pi_hal_end <-pi_hal[,-1] %>% setNames(as.character(1891:2120))
-# dim(U_hal_end);names(U_hal_end)
-
-# time invariant ----------------------------------------------------------
-
-### data: survival probability and fertility by age for Japan
-# available at https://www.demographic-research.org/volumes/vol41/24/default.htm
-
-p_1947 <- 1 - read.csv("tests/qx_years.csv", header = F, sep = " ")[[4]]
-f_1947 <- read.csv("tests/fx_years.csv", header = F, sep = " ")[[4]]
-p_2014 <- 1 - read.csv("tests/qx_years.csv", header = F, sep = " ")[[205]]
-f_2014 <- read.csv("tests/fx_years.csv", header = F, sep = " ")[[205]]
-
-# Caswell assumption on first age
-f_1947 <- f_1947 * p_1947
-f_2014 <- f_2014 * p_2014
-
-kins_japan_1947 <- kin(p_1947, f_1947, living = F)$kin_full
-kins_japan_1947 %>%
-  filter(alive=="yes", kin=="ggm") %>%
-  group_by(age_focal) %>% summarise(sum(count))
-
-
-### results
-kins_japan <- rbind(tibble(Year = 1947, kin(p_1947, f_1947, living = F)$kin_full),
-                    tibble(Year = 2014, kin(p_2014, f_2014, living = F)$kin_full))
-
-# kins alive by age when ego is aged 30 or 70
-kins_japan %>%
-  filter(age_focal %in% c(30,70), alive=="yes") %>%
-  ggplot() +
-  geom_line(aes(x=age_kin, y=count,
-                color=factor(age_focal), linetype=factor(Year)))  +
-  facet_wrap(~kin,scales = "free_y") +
-  theme_classic() +
-  facet_wrap(~kin,scales = "free_y")
-
-kins_japan %>%
-  filter(age_focal %in% 30, alive=="yes", kin == "m", Year==2014)
-
-### get paper results: done with https://plotdigitizer.com/app
-
-m_30_2014 <- c(48.124993716677295, 0.0068724848600042006,
-               52.13541022398433, 0.022765097394085585,
-               56.14582673129136, 0.056697985757917374,
-               60.04166165822103, 0.07398657677157613,
-               64.16665974590543, 0.054765100671140945,
-               68.17707625321246, 0.02330201014576342,
-               71.95832959976478, 0.0035436192454907658) %>% matrix(ncol=2, byrow = T) %>%
-  as.data.frame() %>% setNames(c("age_kin","count")) %>%
-  mutate(kin = "m", year = 2014, age = 30)
-m_30_1947 <- c(47.89583055592257, 0.010630874121749168,
-               51.791665482852245, 0.045100671140939595,
-               56.37499863406029, 0.05111409232120386,
-               59.92708007784368, 0.03908724586435613,
-               63.82291500477335, 0.02577181208053692,
-               68.17707625321246, 0.012671136024014266,
-               71.84374801938742, 0.0025771828465813683) %>% matrix(ncol=2, byrow = T) %>%
-  as.data.frame() %>% setNames(c("age_kin","count")) %>%
-  mutate(kin = "m", year = 1947, age = 30)
-s_30_1947 <- c(117.30467373388943, 0.028030307190039253,
-               3.8671896129379024, 0.0011363654972982584,
-               8.05663990468026, 0.005681817853417949,
-               12.031249743886312, 0.01792929767285178,
-               16.220700035628674, 0.036111111913867226,
-               19.873045098211307, 0.05782828333912762,
-               23.84765903523633, 0.07626262618964844,
-               28.037105229159724, 0.08244949644554042,
-               32.119139392452894, 0.06717171906914071,
-               36.09374513383999, 0.044696973856705915,
-               39.853510422690775, 0.024621210698144477,
-               43.93554458598395, 0.010858592937435215,
-               47.80273010110288, 0.00303030478177092) %>% matrix(ncol=2, byrow = T) %>%
-  as.data.frame() %>% setNames(c("age_kin","count")) %>%
-  mutate(kin = "s", year = 1947, age = 30)
-s_70_1947 <- c(43.93554458598395, 0.0011363654972982584,
-               47.80273010110288, 0.00441919166376952,
-               52.20702494320051, 0.013383845316732092,
-               56.074218653957374, 0.026388889290266948,
-               59.94140416907631, 0.03952020358922534,
-               64.02343013673156, 0.048358586916764375,
-               68.10546430002474, 0.045959600046354354,
-               71.97264981514367, 0.03143939886539736,
-               76.05468397843684, 0.015404045293554923,
-               80.02928971982392, 0.005429296468717607,
-               84.00390365684893, 0.0011363654972982584) %>% matrix(ncol=2, byrow = T) %>%
-  as.data.frame() %>% setNames(c("age_kin","count")) %>%
-  mutate(kin = "s", year = 1947, age = 70)
-s_30_2014 <- c(7.949219678412107, 0.0005050524024740438,
-               12.031249743886312, 0.0032828357995446046,
-               16.005859583092366, 0.009217175037662914,
-               19.873045098211307, 0.020328289359798468,
-               23.6328103870621, 0.03194444645133473,
-               27.822264776623417, 0.03888889049440111,
-               32.01171916618474, 0.0337121250434572,
-               35.77148445503553, 0.0215909155494469,
-               39.96093884459685, 0.010732322612011683,
-               44.04296481225208, 0.0032828357995446046)%>% matrix(ncol=2, byrow = T) %>%
-  as.data.frame() %>% setNames(c("age_kin","count")) %>%
-  mutate(kin = "s", year = 2014, age = 30)
-s_70_2014 <- c(51.88476426439605, 0.002904044089420749,
-               56.18163888022552, 0.009343435730013084,
-               59.83398394280815, 0.01944444524720057,
-               64.13085855863763, 0.03068182026168629,
-               68.21288452629288, 0.035479798819043,
-               71.75780936260736, 0.03068182026168629,
-               76.16210420470499, 0.019318184554850383,
-               79.92186949355577, 0.008459601250488509,
-               84.00390365684893, 0.00265152270472039) %>% matrix(ncol=2, byrow = T) %>%
-  as.data.frame() %>% setNames(c("age_kin","count")) %>%
-  mutate(kin = "s", year = 2014, age = 70)
-
-output_time_invariant <- m_30_2014 %>%
-  bind_rows(m_30_1947, s_30_1947, s_70_1947, s_30_2014, s_70_2014) %>%
-  mutate(age_kin = trunc(age_kin), count=round(count,7))
-
-compare_time_invariant <- kins_japan %>%
-  filter(kin %in% c("os", "ys"), alive == "yes") %>%
-  group_by(Year, age_focal, age_kin, alive) %>%
-  summarise(count = sum(count)) %>%
-  mutate(kin = "s") %>%
-  bind_rows(kins_japan %>% filter(alive == "yes")) %>%
-  select(-year, -cohort, -alive) %>%
-  rename(count_demokin = count, year = Year) %>%
-  mutate(count_demokin = round(count_demokin,7)) %>%
-  right_join(output_time_invariant %>%
-               rename(age_focal=age, count_paper = count))
-
-compare_time_invariant %>%
-  ggplot() +
-  geom_line(aes(age_kin, count_demokin, linetype=factor(year)), col=1)+
-  geom_line(aes(age_kin, count_paper, linetype=factor(year)), col=2) +
-  facet_grid(~kin+age_focal)+
-  theme_bw()
-
-
-### compare values
-
-
-
-
-
-
-
-
-
-
-# period
-swe_kin_period <- kin(U = U_caswell_2021, f = f_caswell_2021, pi = pi_caswell_2021, stable = F, birth_female = 1,
-                      focal_year = c(1891,1921,1951,2010,2050,2080,2120),
-                      selected_kin = c("d","gd","ggd","m","gm","ggm","os","ys","oa","ya"))
-
-swe_kin_period$kin_summary %>%
-  ggplot(aes(age_focal,count,color=factor(year))) +
-  geom_line(size=1)+
-  scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+
-  facet_wrap(~kin, scales = "free")+
-  theme_bw()
-
-# ADDITIONAL PLOTS cohrot and period
-ggplot(swe_kin_cohorts$kin_summary %>% filter(cohort == 1911),
-       aes(year,mean_age)) +
-  geom_point(aes(size=count,color=kin)) +
-  geom_line(aes(color=kin)) +
-  scale_y_continuous(name = "Edad", breaks = seq(0,110,10), labels = seq(0,110,10), limits = c(0,110))+
-  geom_segment(x = 1911, y = 0, xend = 2025, yend = 110, color = 1)+
-  geom_vline(xintercept = 1911, linetype=2)+
-  theme_light()+ coord_fixed()+
-  labs(title = "Kin cohort 1911")
-
-swe_kin_period$kin_summary %>%
-  filter(age_focal==50) %>%
-  ggplot(aes(year, mean_age, color=kin)) +
-  geom_point(aes(size=count)) +
-  geom_line() +
-  geom_hline(yintercept = 50, color=1, linetype=1)+
-  theme_light()+
-  coord_fixed()+
-  labs(title = "Kin period")
-
-### plots
-# kins alive by age when ego is aged 30 or 70
-kins_japan %>%
-  filter(age_focal %in% c(30,70), alive=="yes") %>%
-  ggplot() +
-  geom_line(aes(x=age_kin, y=count,
-                color=factor(age_focal), linetype=factor(Year)))  +
-  facet_wrap(~kin,scales = "free_y") +
-  theme_classic() +
-  facet_wrap(~kin,scales = "free_y")
-# kins alive during ego´s life
-kins_japan %>%
-  filter(alive=="yes") %>%
-  group_by(Year, kin, age_focal) %>% summarise(count = sum(count)) %>%
-  ggplot() +
-  geom_line(aes(age_focal, count, linetype=factor(Year)))  +
-  theme_classic() +
-  facet_wrap(~kin, scales = "free_y")
-# experienced deaths
-kins_japan %>%
-  filter(alive=="no") %>%
-  group_by(Year, kin, age_focal) %>% summarise(count = sum(count)) %>%
-  ggplot() +
-  geom_line(aes(age_focal, count, linetype=factor(Year)))  +
-  theme_classic() +
-  facet_wrap(~kin, scales = "free_y")
-# variation coefficient of age by kin
-kins_japan %>%
-  filter(alive=="yes") %>%
-  group_by(Year, kin, age_focal) %>%
-  summarise(mean_age = sum(count*age_kin)/sum(count),
-            var_age  = sum(count*age_kin^2)/sum(count) - mean_age^2,
-            cv_age  = round(sqrt(var_age)/mean_age*100,1)) %>%
-  ggplot() +
-  geom_line(aes(age_focal, cv_age, linetype=factor(Year)))  +
-  theme_classic() +
-  facet_wrap(~kin, scales = "free_y")
-# dependency ages
-kins_japan %>%
-  filter(alive=="yes") %>%
-  mutate(age_kin_dep = ifelse(age_kin<15,"0-14",
-                              ifelse(age_kin<65,"15-64","65+"))) %>%
-  group_by(Year, kin, age_focal, age_kin_dep) %>%
-  summarise(count = sum(count)) %>%
-  ggplot() +
-  geom_line(aes(age_focal, count,
-                color = age_kin_dep, linetype=factor(Year)))  +
-  theme_classic() +
-  facet_wrap(~kin, scales = "free_y")
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-swe_surv_2010 <- swe_surv %>% pull(`2011`)
-swe_asfr_2010 <- swe_asfr %>% pull(`2011`)
-debugonce(kin)
-swe50_2015_stable <- kin(U = swe_surv_2010, f = swe_asfr_2010, output_cohort = c(1911,1930),
-                         output_kin = c("d","m"))
-
-swe_kin_cohorts <- kin(U = U_caswell_2021, f = f_caswell_2021, time_invariant = F,
-                       birth_female = 1,
-                       output_cohort = c(1911),
-                       output_kin = c("d"))
-
-U = U_caswell_2021; f = f_caswell_2021; pi = pi_caswell_2021; birth_female = 1;
-output_cohort = c(1911);output_period = NULL; output_kin = c("d")
-
-# FIGURE 5
-
-
-
diff --git a/dev/tests/repl_caswell_first_year.R b/dev/tests/repl_caswell_first_year.R
deleted file mode 100644
index c88c121..0000000
--- a/dev/tests/repl_caswell_first_year.R
+++ /dev/null
@@ -1,106 +0,0 @@
-# replicating Caswell´s figures: choose some kin
-library(DemoKin)
-library(tidyverse)
-library(R.matlab)
-source("R/kin_time_invariant.R")
-
-# paper input from https://www.demographic-research.org/volumes/vol45/16/45-16.pdf
-input_time_variant <- readMat("tests/SWEhist_matrices.mat")
-
-# check structure from reading mat
-class(input_time_variant)
-names(input_time_variant)
-length(input_time_variant[["matrices"]]) # number of years
-input_time_variant[["matrices"]][[128]][[1]][[1]] # U
-input_time_variant[["matrices"]][[1]][[1]][[2]] # F
-input_time_variant[["matrices"]][[1]][[1]][[3]] # popsize
-input_time_variant[["matrices"]][[1]][[1]][[4]] # pi
-length(input_time_variant_proj[["matrices"]]) # number of years
-
-# reshape
-U_hal <- f_hal <-N_hal <- pi_hal <-matrix(rep(0,111))
-for(y in 1:128){
-  U <- input_time_variant[["matrices"]][[y]][[1]][[1]] %>% as.matrix()
-  f <- input_time_variant[["matrices"]][[y]][[1]][[2]] %>% as.matrix()
-  N <- input_time_variant[["matrices"]][[y]][[1]][[3]] %>% as.matrix()
-  pi <- input_time_variant[["matrices"]][[y]][[1]][[4]] %>% as.matrix()
-  U_hal <- cbind(U_hal, c(U[col(U)==row(U)-1], U[ncol(U),nrow(U)]))
-  f_hal <- cbind(f_hal ,f[1,])
-  N_hal <- cbind(N_hal ,N)
-  pi_hal <-cbind(pi_hal, pi)
-}
-U_hal <- U_hal[,-1]
-f_hal <- f_hal[,-1]
-N_hal <- N_hal[,-1]
-pi_hal <-pi_hal[,-1]
-colnames(U_hal) <- colnames(f_hal) <- colnames(N_hal) <- colnames(pi_hal) <-1891:2018
-dim(U_hal);class(U_hal %>% as.matrix)
-
-# output from Hal (dropbox link https://www.dropbox.com/t/3YiILmn7SpczN3oM)
-output_time_variant <- readMat("tests/time-varying_sweden.mat")
-
-# inspect the way the package reads mat
-class(output_time_variant)
-names(output_time_variant)
-length(output_time_variant[["allkin"]]) # number of years
-length(output_time_variant[["allkin"]][[1]])
-length(output_time_variant[["allkin"]][[1]])
-class(output_time_variant[["allkin"]][[1]][[1]]) # 1 array with kin matrix
-dim(output_time_variant[["allkin"]][[1]][[1]][,,14]) # the matrix of the nth kin, 111 ages
-
-# use own codes to interpret
-codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
-caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n")
-
-# re shape data to tidy
-output_time_variant_df <- map_df(1:128, function(i){
-  array_branch(output_time_variant[["allkin"]][[i]][[1]], margin = 3) %>%
-    map_df(., as.data.frame)}) %>%
-  setNames(as.character(0:110)) %>%
-  bind_cols(crossing(year = 1891+(0:127), # years
-                     kin_index = 1:14, # number of possible kin
-                     age_kin = 0:110) # ages
-            ) %>%
-  inner_join(tibble(kin = codes, caswell_codes) %>%
-               arrange(caswell_codes) %>% mutate(kin_index = 1:14))
-
-# check dimension: 128 years, 14 types of kin, 111 ages
-nrow(output_time_variant_df); 128*14*111
-
-# own calculation for first year
-out_first_year <- kin_time_invariant(
-                      U = U_hal[,"1891"],
-                      f = f_hal[,"1891"],
-                      pi = pi_hal[,"1891"],
-                      birth_female = 1)
-
-# check first visually demokin
-out_first_year %>%
-  filter(alive == "yes") %>%
-  group_by(age_focal, kin) %>%
-  summarise(count = sum(count, na.rm=T)) %>%
-  ggplot(aes(age_focal, count)) +
-  geom_line() +
-  facet_wrap(~kin, scales="free_y")
-
-# compare with paper results
-comparison <- out_first_year %>%
-  filter(alive == "yes") %>%
-  group_by(age_focal, kin) %>%
-  summarise(count = sum(count, na.rm=T)) %>%
-  mutate(source = "demokin") %>%
-  bind_rows(
-    output_time_variant_df %>%
-      filter(year %in% 1891) %>%
-      pivot_longer(`0`:`110`, names_to = "age", values_to = "count") %>%
-      mutate(age = as.integer(age)) %>%
-      group_by(age_focal=age, kin) %>%
-      summarise(count = sum(count)) %>%
-      mutate(source = "paper"))
-
-# comparison visually
-comparison %>%
-  ggplot() +
-  geom_line(aes(age_focal, count, color=source, linetype=source)) +
-  facet_wrap(~kin, scales="free_y") +
-  theme_bw()
diff --git a/dev/tests/timevarying_kin.m b/dev/tests/timevarying_kin.m
deleted file mode 100644
index c1176c8..0000000
--- a/dev/tests/timevarying_kin.m
+++ /dev/null
@@ -1,180 +0,0 @@
-function kout=timevarying_kin(U,F,pi,om,pkin);
-% function to return kinship network
-% calculated from the rates and the kinship at the previous time
-% U=survival matrix
-% F=fertility matrix
-% pi = distribution of ages of mothers
-% om=number of age classes
-% pkin = the array of all kin from the previous time step
-% model structure
-% k(x+1,t+1)=U(t)*k(x,t) + F(t)*kstar(x,t) for some other kin kstar
-
-%set to full in case they arrive as sparse matrices
-U=full(U);
-F=full(F);
-pi=full(pi);
-
-%frequently used zero vector for initial condition
-zvec=zeros(om,1);
-I=eye(om);
-omz=om-1;
-
-% a: daughters of focal
-
-A(:,1)=zvec;
-for ix=1:omz
-    ap=U*pkin.A(:,ix) + F*I(:,ix);
-    A(:,ix+1)=ap;
-    
-end % for ix
-
-% b = granddaughters of Focal
-
-B(:,1)=zvec;
-for ix=1:omz
-    bp=U*pkin.B(:,ix) + F*pkin.A(:,ix);
-    B(:,ix+1)=bp;
-    
-end
-
-
-% c = greatgranddaughters of Focal
-C(:,1)=zvec;
-for ix=1:omz
-    cp=U*pkin.C(:,ix) +F*pkin.B(:,ix);
-    C(:,ix+1)=cp;
-    
-end
-
-
-% d = mothers of Focal
-D(:,1)=pi;
-for ix=1:omz
-    dp=U*pkin.D(:,ix) + 0;
-    D(:,ix+1)=dp;
-    
-end
-
-
-% g = grandmothers of Focal
-%only maternal grandmothers right now
-G(:,1)=pkin.D*pi;;
-for ix=1:omz
-    gp=U*pkin.G(:,ix) + 0;
-    G(:,ix+1)=gp;
-    
-end
-
-
-% h = greattrandmothers of Focal
-
-H(:,1)=pkin.G*pi;
-for ix=1:omz
-    hp=U*pkin.H(:,ix) + 0;
-    H(:,ix+1)=hp;
-    
-end
-
-% m = older sisters of Focal
-
-M(:,1)=pkin.A*pi;
-for ix=1:omz
-    mp=U*pkin.M(:,ix) + 0;
-    M(:,ix+1)=mp;
-    
-end
-
-% n = younger sisters
-
-N(:,1)=zvec;
-for ix=1:omz
-    np=U*pkin.N(:,ix) + F*pkin.D(:,ix);
-    N(:,ix+1)=np;
-    
-end
-
-
-% p = nieces through older sisters
-
-P(:,1)=pkin.B*pi;
-for ix=1:omz
-    pp=U*pkin.P(:,ix) + F*pkin.M(:,ix);
-    P(:,ix+1)=pp;
-end
-
-% q = nieces through younger sisters
-
-Q(:,1)=zvec;
-for ix=1:omz
-    qp=U*pkin.Q(:,ix) + F*pkin.N(:,ix);
-    Q(:,ix+1)=qp;
-    
-end
-
-% r = aunts older than mother
-
-R(:,1)=pkin.M*pi;
-for ix=1:omz
-    rp=U*pkin.R(:,ix) + 0;
-    R(:,ix+1)=rp;
-    
-end
-
-% s = aunts younger than mother
-
-S(:,1)=pkin.N*pi;
-for ix=1:omz
-    sp=U*pkin.S(:,ix) + F*pkin.G(:,ix);
-    S(:,ix+1)=sp;
-    
-end
-
-% t = cousins from older aunts
-
-T(:,1)=pkin.P*pi;
-for ix=1:omz
-    tp=U*pkin.T(:,ix) + F*pkin.R(:,ix);
-    T(:,ix+1)=tp;
-    
-end
-
-
-% v = cousins from aunts younger than mother
-
-V(:,1)=pkin.Q*pi;
-for ix=1:omz
-    vp=U*pkin.V(:,ix) + F*pkin.S(:,ix);
-    V(:,ix+1)=vp;
-    
-end
-
-%concatenate kin matrices
-allkin=cat(3,A,B,C,D,G,H,M,N,P,Q,R,S,T,V);
-
-%concatenate, combining older and younger sisters, etc.
-allkin2=cat(3,A,B,C,D,G,H,M+N,P+Q,R+S,T+V);
-
-%create output structures
-kout.A=A;
-kout.B=B;
-kout.C=C;
-kout.D=D;
-kout.G=G;
-kout.H=H;
-kout.M=M;
-kout.N=N;
-kout.P=P;
-kout.Q=Q;
-kout.R=R;
-kout.S=S;
-kout.T=T;
-kout.V=V;
-
-kout.allkin=allkin;
-kout.allkin2=allkin2;
-
-kout.U=U;
-kout.F=F;
-kout.pi=pi;
-
- 
\ No newline at end of file
diff --git a/DemoKin-Logo.png b/man/figures/DemoKin-Logo.png
similarity index 100%
rename from DemoKin-Logo.png
rename to man/figures/DemoKin-Logo.png
diff --git a/man/get_HMDHFD.Rd b/man/get_HMDHFD.Rd
deleted file mode 100644
index 9bbf264..0000000
--- a/man/get_HMDHFD.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/get_HMDHFD.R
-\name{get_HMDHFD}
-\alias{get_HMDHFD}
-\title{Get time serie matrix data from HMD/HFD}
-\usage{
-get_HMDHFD(
-  country = "SWE",
-  min_year = 1900,
-  max_year = 2018,
-  user_HMD = NULL,
-  pass_HMD = NULL,
-  user_HFD = NULL,
-  pass_HFD = NULL,
-  OAG = 100
-)
-}
-\arguments{
-\item{country}{numeric. Country code from rom HMD/HFD.}
-
-\item{min_year}{integer. Older year to get data.}
-
-\item{max_year}{numeric. Latest year to get data.}
-
-\item{user_HMD}{character. From HMD.}
-
-\item{pass_HMD}{character. From HMD.}
-
-\item{user_HFD}{character. From HFD.}
-
-\item{pass_HFD}{character. From HFD.}
-
-\item{OAG}{numeric. Open age group to standarize output.}
-}
-\value{
-A list wiith female survival probability, survival function, fertility and poopulation age specific matrixes, with calendar year as colnames.
-}
-\description{
-Wrapper function to get data of female survival, fertlity and population
-of selected country on selected period.
-}
diff --git a/man/kin.Rd b/man/kin.Rd
index 036bdd9..62ec76b 100644
--- a/man/kin.Rd
+++ b/man/kin.Rd
@@ -40,6 +40,8 @@ in a more general perspective) with rows as ages (and columns as years in case o
 this needs to be set as 1.}
 
 \item{stable}{logic. Deprecated. Use \code{time_invariant}.}
+
+\item{U}{logic. Deprecated. Use \code{p}.}
 }
 \value{
 A list with:
diff --git a/man/kin2sex.Rd b/man/kin2sex.Rd
index 34bf7c1..d91b032 100644
--- a/man/kin2sex.Rd
+++ b/man/kin2sex.Rd
@@ -22,9 +22,9 @@ kin2sex(
 )
 }
 \arguments{
-\item{pf}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+\item{pf}{numeric. A vector (atomic) or  matrix with female probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
 
-\item{pm}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+\item{pm}{numeric. A vector (atomic) or  matrix with male probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
 
 \item{ff}{numeric. Same as pf but for fertility rates.}
 
@@ -49,13 +49,12 @@ kin2sex(
 \item{output_period}{integer. Vector of period years for returning results. Should be within input data years range.}
 
 \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
-
-\item{stable}{logic. Deprecated. Use \code{time_invariant}.}
 }
 \value{
 A list with:
 \itemize{
-\item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), including living and dead kin at that age.}
+\item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example \code{d} could be daughter or son depending \code{sex_kin},
+\code{oa} is older aunts or uncles also depending \code{sex_kin} value, etc.), including living and dead kin at that age.}
 \item{kin_summary}{ a data frame with Focal´s age, related ages, sex and type of kin, with indicators obtained processing \code{kin_full}, grouping by cohort or period (depending on the given arguments):}
 {\itemize{
 \item{\code{count_living}}{: count of living kin at actual age of Focal}
@@ -71,7 +70,8 @@ A list with:
 \description{
 Implementation of two-sex matrix kinship model. This produces kin counts grouped by kin, age and sex of
 each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
-are grouped in one male count of cousins.
+are grouped in one male count of cousins. Note that the output labels relative following female notation: the label \code{m}
+refers to either mothers or fathers, and column \code{sex_kin} determine the sex of the relative.
 }
 \details{
 See Caswell (2022) for details on formulas.
diff --git a/man/kin_time_variant.Rd b/man/kin_time_variant.Rd
index 17c1f85..0646778 100644
--- a/man/kin_time_variant.Rd
+++ b/man/kin_time_variant.Rd
@@ -23,6 +23,8 @@ kin_time_variant(
 
 \item{pi}{numeric. A matrix with distribution of childbearing with rows as ages and columns as years. Coincident with \code{U}.}
 
+\item{n}{numeric. A matrix of population with rows as ages and columns as years. Coincident with \code{U}.}
+
 \item{output_cohort}{integer. Year of birth of focal to return as output. Could be a vector. Should be within input data years range.}
 
 \item{output_period}{integer. Year for which to return kinship structure. Could be a vector. Should be within input data years range.}
@@ -32,8 +34,6 @@ kin_time_variant(
 \item{birth_female}{numeric. Female portion at birth.}
 
 \item{list_output}{logical. Results as a list with years elements (as a result of \code{output_cohort} and \code{output_period} combination), with a second list of \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
-
-\item{N}{numeric. A matrix of population with rows as ages and columns as years. Coincident with \code{U}.}
 }
 \value{
 A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives
diff --git a/man/kin_time_variant_2sex.Rd b/man/kin_time_variant_2sex.Rd
index bcb3ca9..a30624f 100644
--- a/man/kin_time_variant_2sex.Rd
+++ b/man/kin_time_variant_2sex.Rd
@@ -48,9 +48,7 @@ kin_time_variant_2sex(
 
 \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
 
-\item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Default \code{TRUE}.}
-
-\item{stable}{logic. Deprecated. Use \code{time_invariant}.}
+\item{list_output}{logical. Results as a list with years elements (as a result of \code{output_cohort} and \code{output_period} combination), with a second list of \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
 }
 \value{
 A data.frame with year, cohort, Focal´s age, related ages, sex and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), including living and dead kin at that age and sex.
diff --git a/man/output_period_cohort_combination.Rd b/man/output_period_cohort_combination.Rd
index e53a40c..c96a235 100644
--- a/man/output_period_cohort_combination.Rd
+++ b/man/output_period_cohort_combination.Rd
@@ -21,5 +21,5 @@ output_period_cohort_combination(
 \description{
 defince apc to return.
 
-defince apc to return.
+define apc combination to return in \code{kin} and \code{kin2sex}.
 }
diff --git a/man/timevarying_kin_2sex.Rd b/man/timevarying_kin_2sex.Rd
index e2ad4ac..d6dccf2 100644
--- a/man/timevarying_kin_2sex.Rd
+++ b/man/timevarying_kin_2sex.Rd
@@ -9,13 +9,16 @@ timevarying_kin_2sex(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
 \arguments{
 \item{Ut}{numeric. A matrix of survival probabilities (or ratios).}
 
-\item{pit}{numeric. A matrix with distribution of childbearing.}
+\item{Ft}{numeric. A matrix of age-specific fertility rates.}
+
+\item{Ft_star}{numeric. Ft but for female fertility.}
+
+\item{pit}{numeric. A matrix with distribution of childbearing.
+sex_focal}
 
 \item{ages}{numeric.}
 
 \item{pkin}{numeric. A list with kin count distribution in previous year.}
-
-\item{ft}{numeric. A matrix of age-specific fertility rates.}
 }
 \description{
 one time projection kin. internal function.
diff --git a/vignettes/Reference.Rmd b/vignettes/Reference_OneSex.Rmd
similarity index 98%
rename from vignettes/Reference.Rmd
rename to vignettes/Reference_OneSex.Rmd
index cd1bee3..1259f8b 100644
--- a/vignettes/Reference.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -1,17 +1,18 @@
 ---
-title: "Expected kin counts by type of relative: A matrix implementation"
+title: "Expected kin counts by type of relative in a one-sex framework"
 output: 
   html_document:
     toc: true
     toc_depth: 1
 vignette: >
-  %\VignetteIndexEntry{Reference}
+  %\VignetteIndexEntry{Reference_OneSex}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
 
 ```{r, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+library(devtools); load_all()
 ```
 
 In this vignette, we'll demonstrate how `DemoKin` can be used to compute kinship networks for an average member of a given (female) population. Let us call her Focal: an average Swedish woman who has always lived in Sweden and whose family has never left the country.
@@ -24,8 +25,7 @@ First, we compute kin counts in a **time-invariant** framework. We assume that F
 In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of survival ratios and another vector of fertility rates. In this example, we get the data for the year 2015, and run the matrix models:
 
 ```{r, message=FALSE, warning=FALSE}
-library(devtools)
-load_all()
+library(DemoKin)
 library(tidyr)
 library(dplyr)
 library(ggplot2)
@@ -56,7 +56,7 @@ head(swe_2015$kin_summary)
 To produce it, we sum over all ages of kin to produce a data frame of expected kin counts by year or cohort and age of Focal (but not by age of kin). 
 Consider this simplified example for living kin counts:
 
-```{r}
+```{r, message=FALSE, warning=FALSE}
 kin_summary_example <- 
   swe_2015$kin_full %>% 
   select(year, cohort, kin, age_focal, age_kin, living, dead) %>% 
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
new file mode 100644
index 0000000..cc8e2c7
--- /dev/null
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -0,0 +1,271 @@
+---
+title: "Two-sex kinship model"
+output: 
+  html_document:
+    toc: true
+    toc_depth: 1
+vignette: >
+  %\VignetteIndexEntry{Reference_TwoSex}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include=FALSE}
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+library(devtools); load_all()
+```
+
+Human males generally live longer and reproduce later than females. 
+These sex-specific processes affect kinship dynamics in a number of ways. 
+For example, the degree to which an average member of the population, call her Focal, has a living grandparent is affected by differential mortality affecting the parental generation at older ages. 
+We may also be interested in considering how kinship structures vary by Focal's sex: a male Focal may have a different number of grandchildren than a female Focal given differences in fertility by sex. 
+Documenting these differences matters since women often face greater expectations to provide support and informal care to relatives.
+As they live longer, they may find themselves at greater risk of being having no living kin.
+The function `kin2sex` implements two-sex kinship models as introduced by Caswell (2022). 
+This vignette show how to run two-sex models and highlights some of the advantages of this model over one-sex models in populations with time-invariant and time-variant rates.
+
+```{r, message=FALSE, warning=FALSE}
+library(DemoKin)
+library(tidyr)
+library(dplyr)
+library(ggplot2)
+library(knitr)
+```
+
+### 1. Demographic rates by sex
+
+Data on female fertility by age is less common than female fertility. Schoumaker (2019) shows that male TFR is almost always higher than female Total Fertility Rates (TFR) using a sample of 160 countries. 
+For this example, we use data from 2012 France to exemplify the use of the two-sex function. 
+Data on female and male fertility and mortality are included in `DemoKin`. In this population, male and female TFR is almost identical (1.98 and 1.99) but the distributions of fertility by sex varies over age:
+
+```{r}
+fra_fert_f <- fra_asfr_sex[,"ff"]
+fra_fert_m <- fra_asfr_sex[,"fm"]
+fra_surv_f <- fra_surv_sex[,"pf"]
+fra_surv_m <- fra_surv_sex[,"pm"]
+
+sum(fra_fert_m)-sum(fra_fert_f)
+
+data.frame(value = c(fra_fert_f, fra_fert_m, fra_surv_f, fra_surv_m),
+           age = rep(0:100, 4),
+           sex = rep(c(rep("f", 101), rep("m", 101)), 2),
+           risk = c(rep("fertility rate", 101 * 2), rep("survival probability", 101 * 2))) %>%
+  ggplot(aes(age, value, col=sex)) + 
+  geom_line() + 
+  facet_wrap(~ risk, scales = "free_y") + 
+  theme_bw()
+```
+
+### 2. Time-invariant two-sex kinship models
+
+We now introduce the functions `kin2sex`, which is similar to the one-sex function `kin` (see `?kin`) with two exceptions. 
+First, the user needs to specify mortality and fertility by sex. 
+Second, the user must indicate the sex of Focal (which was assumed to be female in the one-sex model).
+Let us first consider the application for time-invariant populations:
+
+```{r}
+kin_result <- kin2sex(
+  pf = fra_surv_f,
+  pm = fra_surv_m, 
+  ff = fra_fert_f, 
+  fm = fra_fert_m, 
+  time_invariant = TRUE,
+  sex_focal = "f", 
+  birth_female = .5
+  )
+```
+
+The output of `kin2sex` is equivalent to that of `kin`, except that it includes a column `sex_kin` to specify the sex of the given relatives. 
+
+Let's group aunts and siblings to visualize the number of living kin by Focal's age.
+
+```{r, message=FALSE, warning=FALSE}
+kin_out <- kin_result$kin_summary %>% 
+  mutate(kin = case_when(kin %in% c("s", "s") ~ "s",
+                         kin %in% c("ya", "oa") ~ "a",
+                         T ~ kin)) %>%
+  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a"))
+
+kin_out %>% 
+  group_by(kin, age_focal, sex_kin) %>%
+  summarise(count=sum(count_living)) %>%
+  ggplot(aes(age_focal, count, fill=sex_kin))+
+  geom_area()+
+  theme_bw() +
+  facet_wrap(~kin)
+```
+
+**A note on terminology**
+
+The function `kin2sex` uses the same codes as `kin` to identify relatives (see `demokin_codes()`).
+Note that when running a two-sex model, the code 'm' refers to either mothers or fathers! 
+Use the column `sex_kin` to determine the sex of a given relatives.
+For example, in order to consider only sons and ignore daughters, use:
+
+```{r}
+kin_result$kin_summary %>% 
+  filter(kin == "d", sex_kin == "m") %>% 
+  head()
+```
+
+Information on kin availability by sex allows us to consider sex ratios, a traditional measure in demography, with females often in denominator. The following figure, for example, shows that a 25yo French woman in our hypothetical population can expect to have 0.5 grandfathers for every grandmother:  
+
+```{r, message=FALSE, warning=FALSE}
+kin_out %>% 
+  group_by(kin, age_focal) %>%
+  summarise(sex_ratio=sum(count_living[sex_kin=="m"], na.rm=T)/sum(count_living[sex_kin=="f"], na.rm=T)) %>%
+  ggplot(aes(age_focal, sex_ratio))+
+  geom_line()+
+  theme_bw() +
+  facet_wrap(~kin, scales = "free")
+```
+
+The experience of kin loss for Focal depends on differences in mortality between sexes. 
+A female Focal starts losing fathers earlier than mothers. 
+We see a slightly different pattern for grandparents since Focal's experience of grandparental loss is dependent on the initial availability of grandparents (i.e. if Focal's grandparent died before her birth, she will never experience his death).
+
+```{r, message=FALSE, warning=FALSE}
+# sex ratio
+kin_out %>%
+  group_by(kin, sex_kin, age_focal) %>%
+  summarise(count=sum(count_dead)) %>%
+  ggplot(aes(age_focal, count, col=sex_kin))+
+  geom_line()+
+  theme_bw() +
+  facet_wrap(~kin)
+```
+
+
+### 3. Time-variant two-sex kinship models
+
+We look at populations where demographic rates are not static but change on a yearly basis. 
+For this, we consider the case of Sweden using data pre-loaded in `DemoKin`.
+For this example, we will create 'pretend' male fertility rates by slightly perturbing the existing female rates. 
+This is a toy example, since a real two-sex model should use actual female and male rates as inputs. 
+
+```{r}
+years <- ncol(swe_px)
+ages <- nrow(swe_px)
+swe_surv_f_matrix <- swe_px
+swe_surv_m_matrix <- swe_px ^ 1.5 # artificial perturbation for this example
+swe_fert_f_matrix <- swe_asfr
+swe_fert_m_matrix <- rbind(matrix(0, 5, years),  
+            swe_asfr[-((ages-4):ages),]) * 1.05 # artificial perturbation for this example
+
+par(mfrow=c(1,2))
+plot(swe_surv_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Survival probability")
+lines(swe_surv_m_matrix[,"1900"], col=2)
+plot(swe_fert_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Fertility rate")
+lines(swe_fert_m_matrix[,"1900"], col=2)
+```
+
+We now run the time-variant two-sex models (note the `time_invariant = FALSE` argument):
+
+```{r}
+kin_out_time_variant <- kin2sex(
+                      pf = swe_surv_f_matrix, 
+                      pm = swe_surv_m_matrix,
+                      ff = swe_fert_f_matrix, 
+                      fm = swe_fert_m_matrix,
+                      sex_focal = "f",
+                      time_invariant = FALSE,
+                      birth_female = .5,
+                      output_cohort = 1900
+                      )
+```
+
+We can plot data on kin availability alongside values coming from a time-invariant model to show how demographic change matters: the time-variant models take into account changes derived from the demographic transition, whereas the time-invariant models assume never-changing rates. 
+
+```{r, message=FALSE, warning=FALSE}
+kin_out_time_invariant <- kin2sex(
+                      swe_surv_f_matrix[,"1900"], swe_surv_m_matrix[,"1900"], 
+                      swe_fert_f_matrix[,"1900"], swe_fert_m_matrix[,"1900"], 
+                      sex_focal = "f", birth_female = .5)
+
+
+kin_out_time_variant$kin_summary %>%
+  filter(cohort == 1900) %>% mutate(type = "variant") %>%
+  bind_rows(kin_out_time_invariant$kin_summary %>% mutate(type = "invariant")) %>% 
+  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
+                         kin %in% c("ya", "oa") ~ "a",
+                         T ~ kin)) %>%
+  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a")) %>%
+  group_by(type, kin, age_focal, sex_kin) %>%
+  summarise(count=sum(count_living)) %>%
+  ggplot(aes(age_focal, count, linetype=type))+
+  geom_line()+ theme_bw() +
+  facet_grid(cols = vars(kin), rows=vars(sex_kin), scales = "free")
+```
+
+### 4. Approximations
+
+Caswell (2022) introduced two approaches for approximating two-sex kinship structures for cases when male demographic rates are not available. 
+The first is the *androgynous* approximation, which assumes equal fertility and survival for males and females.
+The second is the use of *GKP factors* apply to each type of relative (e.g., multiplying mothers by two to obtain the number of mothers and fathers). 
+
+Here, we present a visual evaluation of the accuracy of these approximations by comparing to 'true' two-sex models using the French data included with `DemoKin` for time-invariant models (Caswell, 2022). 
+We start by considering the androgynous approximation.
+We compare expected kin counts by age and find high levels of consistency for all kin types, except for grandfathers and great-grandfathers:
+
+```{r, message=FALSE, warning=FALSE}
+kin_out <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
+
+kin_out_androgynous <- kin2sex(fra_surv_f, fra_surv_f, fra_fert_f, fra_fert_f, sex_focal = "f", birth_female = .5)
+
+bind_rows(
+  kin_out$kin_summary %>% mutate(type = "full"),
+  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous")) %>%
+  group_by(kin, age_focal, sex_kin, type) %>%
+  summarise(count = sum(count_living)) %>%
+  ggplot(aes(age_focal, count, linetype = type)) +
+  geom_line() +
+  theme_bw() +
+  theme(legend.position = "bottom", axis.text.x = element_blank()) +
+  facet_grid(row = vars(sex_kin), col = vars(kin), scales = "free")
+```
+
+Next, we consider the use of GKP factors and find that it also approximates relatively accurately kin counts at different ages of Focal.
+These are presented as examples only. 
+Users are invited to perform more rigorous comparisons of these approximations. 
+
+```{r, message=FALSE, warning=FALSE}
+# with gkp
+kin_out_1sex <- kin(fra_surv_f, fra_fert_f, birth_female = .5)
+
+kin_out_GKP <- kin_out_1sex$kin_summary%>%
+  mutate(count_living = case_when(kin == "m" ~ count_living * 2,
+                            kin == "gm" ~ count_living * 4,
+                            kin == "ggm" ~ count_living * 8,
+                            kin == "d" ~ count_living * 2,
+                            kin == "gd" ~ count_living * 4,
+                            kin == "ggd" ~ count_living * 4,
+                            kin == "oa" ~ count_living * 4,
+                            kin == "ya" ~ count_living * 4,
+                            kin == "os" ~ count_living * 2,
+                            kin == "ys" ~ count_living * 2,
+                            kin == "coa" ~ count_living * 8,
+                            kin == "cya" ~ count_living * 8,
+                            kin == "nos" ~ count_living * 4,
+                            kin == "nys" ~ count_living * 4))
+
+bind_rows(
+  kin_out$kin_summary %>% mutate(type = "full"),
+  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous"),
+  kin_out_GKP %>% mutate(type = "gkp")) %>%
+  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
+                          kin %in% c("ya", "oa") ~ "a",
+                          kin %in% c("coa", "cya") ~ "c",
+                          kin %in% c("nys", "nos") ~ "n",
+                          T ~ kin)) %>%
+  filter(age_focal %in% c(5, 15, 30, 60, 80)) %>%
+  group_by(kin, age_focal, type) %>%
+  summarise(count = sum(count_living)) %>%
+  ggplot(aes(type, count)) +
+  geom_bar(aes(fill=type), stat = "identity") +
+  theme_bw()+theme(axis.text.x = element_text(angle = 90), legend.position = "bottom")+
+  facet_grid(col = vars(kin), row = vars(age_focal), scales = "free")
+```
+
+## References
+
+Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359–396. 

From 1a14b1c6f9c9d36dfddd36814b8d8d6992d60e06 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 8 Feb 2023 14:55:40 -0300
Subject: [PATCH 09/89] when pi added in 2sex variant

---
 R/kin_time_variant_2sex.R                |   4 ++--
 man/figures/README-unnamed-chunk-4-1.png | Bin 199981 -> 538108 bytes
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index 89efe85..e9448da 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -46,8 +46,8 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
   zeros        <- matrix(0, nrow=ages, ncol=ages)
 
   # age distribution at childborn
-  Pif <- pif
-  Pim <- pim
+  Pif <- pif; no_Pif <- FALSE
+  Pim <- pim; no_Pim <- FALSE
   if(is.null(pif)){
     if(!is.null(nf)){
       Pif <- rbind(t(t(nf * ff)/colSums(nf * ff)), matrix(0,ages,length(years_data)))
diff --git a/man/figures/README-unnamed-chunk-4-1.png b/man/figures/README-unnamed-chunk-4-1.png
index 118cd3c262e7dd264e55b45ceadc9e213bae6fa7..681db7c72a85d0413808359a3f75b29e5a27bd32 100644
GIT binary patch
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From b48917043b04e4b0276541e5337063fb658f67c5 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 10 Feb 2023 11:39:11 -0300
Subject: [PATCH 10/89] fix vector sex_kin

---
 R/kin2sex.R               | 8 +-------
 R/kin_time_variant_2sex.R | 7 +++----
 2 files changed, 4 insertions(+), 11 deletions(-)

diff --git a/R/kin2sex.R b/R/kin2sex.R
index a9b2aa3..1401008 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -92,13 +92,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   }
 
   # reorder
-  kin_full <- kin_full %>%
-    dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead) %>%
-    dplyr::mutate(kin_group =  dplyr::case_when(kin %in% c("ys", "os") ~ "s",
-                                                kin %in% c("ya", "oa") ~ "a",
-                                                kin %in% c("coa", "cya") ~ "c",
-                                                kin %in% c("nys", "nos") ~ "n",
-                                                T ~ kin))
+  kin_full <- kin_full %>%dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead)
 
   # summary
   # select period/cohort
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index e9448da..a0bc808 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -148,12 +148,12 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
   kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
     X <- purrr::map2(X, names(X), function(x,y){
-      x <- as.data.frame(x)
+      x <- data.table::as.data.table(x)
       x$year <- Y
       x$kin <- y
       x$sex_kin <- rep(c(rep("f",ages), rep("m",ages)),2)
-      x$age_kin <- rep(age,2)
-      x$alive <- c(rep("living",ages), rep("dead",ages))
+      x$age_kin <- rep(agess, 2)
+      x$alive <- c(rep("living",agess), rep("dead",agess))
       return(x)
     }) %>%
       data.table::rbindlist() %>%
@@ -220,7 +220,6 @@ timevarying_kin_2sex<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
   cya[1:agess,1] = pkin[["nys"]][1:agess,] %*% (pif + pim)
 
   for (ix in 1:om){
-    # ix = 1
     phi[,ix+1] = Gt %*% phi[, ix]
     d[,ix+1]   = Ut %*% pkin[["d"]][,ix]   + Ft %*% phi[,ix]
     gd[,ix+1]  = Ut %*% pkin[["gd"]][,ix]  + Ft %*% pkin[["d"]][,ix]

From 2dcf8998d3f0879ef6f6715fbe0b7b6bfba0392c Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 10 Feb 2023 12:01:14 -0300
Subject: [PATCH 11/89] dtplyr conflict

---
 R/kin2sex.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/kin2sex.R b/R/kin2sex.R
index 1401008..4767d93 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -92,7 +92,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   }
 
   # reorder
-  kin_full <- kin_full %>%dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead)
+  kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead)
 
   # summary
   # select period/cohort
@@ -107,14 +107,14 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   agrupar <- c("age_focal", "kin", "sex_kin", agrupar)
 
   kin_summary <- dplyr::bind_rows(
-    kin_full %>%
+    as.data.frame(kin_full) %>%
       dplyr::rename(total=living) %>%
       dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
       dplyr::summarise(count_living = sum(total),
                 mean_age = sum(total*age_kin)/sum(total),
                 sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
       tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", "value"),
-    kin_full %>%
+    as.data.frame(kin_full) %>%
       dplyr::rename(total=dead) %>%
       dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
       dplyr::summarise(count_dead = sum(total)) %>%

From 323128be3ac7cb3947b5c38168706893e1a16ccf Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 15 Feb 2023 13:25:54 -0300
Subject: [PATCH 12/89] age_kin correction

---
 R/kin_time_variant_2sex.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index a0bc808..ea0ab6a 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -50,7 +50,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
   Pim <- pim; no_Pim <- FALSE
   if(is.null(pif)){
     if(!is.null(nf)){
-      Pif <- rbind(t(t(nf * ff)/colSums(nf * ff)), matrix(0,ages,length(years_data)))
+      Pif <- t(t(nf * ff)/colSums(nf * ff))
     }else{
       Pif <- matrix(0, nrow=ages, ncol=n_years_data)
       no_Pif <- TRUE
@@ -58,7 +58,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
   }
   if(is.null(pim)){
     if(!is.null(nm)){
-      Pim <- rbind(t(t(nm * fm)/colSums(nm * fm)), matrix(0,ages,length(years_data)))
+      Pim <- t(t(nm * fm)/colSums(nm * fm))
     }else{
       Pim <- matrix(0, nrow=ages, ncol=n_years_data)
       no_Pim <- TRUE
@@ -152,7 +152,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
       x$year <- Y
       x$kin <- y
       x$sex_kin <- rep(c(rep("f",ages), rep("m",ages)),2)
-      x$age_kin <- rep(agess, 2)
+      x$age_kin <- rep(age, 4)
       x$alive <- c(rep("living",agess), rep("dead",agess))
       return(x)
     }) %>%

From 135129aed04bb0f72430895334ea322defbf4cc1 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 10 Mar 2023 12:56:00 -0300
Subject: [PATCH 13/89] pivot_longer specify values_to

---
 R/kin.R             | 4 ++--
 R/kin2sex.R         | 4 ++--
 man/plot_diagram.Rd | 8 ++++----
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/R/kin.R b/R/kin.R
index cdc27d7..73f74be 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -110,7 +110,7 @@ kin <- function(p = NULL, f = NULL,
       dplyr::summarise(count_living = sum(total),
                 mean_age = sum(total*age_kin)/sum(total),
                 sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
-      tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", "value"),
+      tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", values_to = "value"),
     kin_full %>%
       dplyr::rename(total=dead) %>%
       dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
@@ -120,7 +120,7 @@ kin <- function(p = NULL, f = NULL,
       dplyr::mutate(count_cum_dead = cumsum(count_dead),
                     mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>%
       dplyr::ungroup() %>%
-      tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", "value")) %>%
+      tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", values_to = "value")) %>%
       dplyr::ungroup() %>%
       tidyr::pivot_wider(names_from = indicator, values_from = value)
 
diff --git a/R/kin2sex.R b/R/kin2sex.R
index 4767d93..08da0c6 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -113,7 +113,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
       dplyr::summarise(count_living = sum(total),
                 mean_age = sum(total*age_kin)/sum(total),
                 sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
-      tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", "value"),
+      tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", values_to = "value"),
     as.data.frame(kin_full) %>%
       dplyr::rename(total=dead) %>%
       dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
@@ -123,7 +123,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
       dplyr::mutate(count_cum_dead = cumsum(count_dead),
                     mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>%
       dplyr::ungroup() %>%
-      tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", "value")) %>%
+      tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", values_to = "value")) %>%
       dplyr::ungroup() %>%
       tidyr::pivot_wider(names_from = indicator, values_from = value)
 
diff --git a/man/plot_diagram.Rd b/man/plot_diagram.Rd
index 8d2448c..fdce2a5 100644
--- a/man/plot_diagram.Rd
+++ b/man/plot_diagram.Rd
@@ -7,13 +7,13 @@
 plot_diagram(kin_total, rounding = 3)
 }
 \arguments{
-\item{kin_total}{data.frame. With columns \code{kin} with type and \code{count} with some measeure.}
+\item{kin_total}{data.frame. values in column \code{kin} define the relative type - see \code{demokin_codes()}. Values in column \code{count} are the expected number of relatives.}
 
-\item{rounding}{numeric. Estimation could have a lot of decimals. Rounding will make looks more clear the diagramm.}
+\item{rounding}{numeric. Number of decimals to show in diagram.}
 }
 \value{
-A plot
+A Keyfitz-style kinship plot.
 }
 \description{
-Given estimation of kin counts from \code{kins} function, draw a network diagramm.
+Draws a Keyfitz-style kinship diagram given a kinship object created by the \code{kin} function. Displays expected kin counts for a Focal aged 'a'.
 }

From 1fe0dfdfc9bdc699e429b1daa49ae5728eb758d4 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 16 Mar 2023 08:39:02 -0300
Subject: [PATCH 14/89] pi for 2 sex fix

---
 R/kin_time_invariant_2sex.R | 33 +++++++++++++--------------------
 R/kin_time_variant_2sex.R   | 22 +++++++++++-----------
 2 files changed, 24 insertions(+), 31 deletions(-)

diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index f864154..7e8fe16 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -54,21 +54,15 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   Ft_star[1:agess,1:ages] <- rbind(birth_female * Ff, (1-birth_female) * Ff)
 
   # parents age distribution under stable assumption in case no input
-  if(is.null(pif)){
-    A = Uf + Ff
-    A_decomp = eigen(A)
-    lambda = as.double(A_decomp$values[1])
-    w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
-    pif = w*A[1,]/sum(w*A[1,])
-    if(all(is.na(pif))) pif <- rep(1/ages, ages)
-  }
-  if(is.null(pim)){
-    A = Um + Fm
-    A_decomp = eigen(A)
-    lambda = as.double(A_decomp$values[1])
-    w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
-    pim = w*A[1,]/sum(w*A[1,])
-    if(all(is.na(pim))) pim <- rep(1/ages, ages)
+  if(is.null(pim) | is.null(pif)){
+  A = Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
+  A_decomp = eigen(A)
+  lambda = as.double(A_decomp$values[1])
+  w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+  wf = w[1:ages]
+  wm = w[(ages+1):(2*ages)]
+  pif = wf * ff / sum(wf * ff)
+  pim = wm * fm / sum(wm * fm)
   }
 
   # initial count matrix (kin ages in rows and focal age in column)
@@ -89,12 +83,12 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   m[1:(agess),1] = c(pif, pim)
   for(i in 1:(ages-1)){
     # i = 1
-    phi[,i+1] = Gt %*% phi[, i]
-    d[,i+1]   = Ut %*% d[,i] + Ft %*% phi[,i]
-    gd[,i+1]  = Ut %*% gd[,i] + Ft %*% d[,i]
+    phi[,i+1] = Gt %*% phi[,i]
+    d[,i+1]   = Ut %*% d[,i]   + Ft %*% phi[,i]
+    gd[,i+1]  = Ut %*% gd[,i]  + Ft %*% d[,i]
     ggd[,i+1] = Ut %*% ggd[,i] + Ft %*% gd[,i]
     m[,i+1]   = Ut %*% m[,i]
-    ys[,i+1]  = Ut %*% ys[,i] + Ft_star %*% m[,i]
+    ys[,i+1]  = Ut %*% ys[,i]  + Ft_star %*% m[,i]
     nys[,i+1] = Ut %*% nys[,i] + Ft %*% ys[,i]
   }
 
@@ -152,7 +146,6 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   ) %>%
     purrr::reduce(rbind)
 
-
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index ea0ab6a..f0b1479 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -45,7 +45,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
   om           <- max(age)
   zeros        <- matrix(0, nrow=ages, ncol=ages)
 
-  # age distribution at childborn
+  # age distribution at child born
   Pif <- pif; no_Pif <- FALSE
   Pim <- pim; no_Pim <- FALSE
   if(is.null(pif)){
@@ -92,18 +92,18 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
     Ft_star[1:agess,1:ages] <- rbind(birth_female * Fft, (1-birth_female) * Fft)
     Fl[[as.character(years_data[t])]] <- Ft
     Fl_star[[as.character(years_data[t])]] <- Ft_star
-    if(no_Pif){
-      A <- Uf + Fft
+    # parents age distribution under stable assumption in case no input
+    if(no_Pim | no_Pif){
+      A = Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
       A_decomp = eigen(A)
-      w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
-      Pif[,t] <- w*A[1,]/sum(w*A[1,])
-    }
-    if(no_Pim){
-      A <- Um + Fmt
-      A_decomp = eigen(A)
-      w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
-      Pim[,t] <- w*A[1,]/sum(w*A[1,])
+      lambda = as.double(A_decomp$values[1])
+      w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+      wf = w[1:ages]
+      wm = w[(ages+1):(2*ages)]
+      Pif[,t] = wf * ff[,t] / sum(wf * ff[,t])
+      Pim[,t] = wm * fm[,t] / sum(wm * fm[,t])
     }
+
     # project
     Ut <- as.matrix(Ul[[t]])
     Ft <- as.matrix(Fl[[t]])

From c80c9d5a7d74dad6f325d978dce34ed0fa9630e4 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 26 Apr 2023 21:40:04 -0300
Subject: [PATCH 15/89] variant no pi fix

---
 DESCRIPTION          | 2 +-
 R/kin_time_variant.R | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 4834c36..6abffee 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -12,7 +12,7 @@ License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 Suggests: 
     knitr,
     rmarkdown,
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index 7440641..8e31031 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -45,6 +45,7 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
       pi_N_null_flag <- TRUE
       pi <- matrix(0, nrow=ages, ncol=n_years_data)
     }else{
+      pi_N_null_flag <- FALSE
       pi <- rbind(t(t(n * f)/colSums(n * f)), matrix(0,ages,length(years_data)))
     }
   }
@@ -63,7 +64,7 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
     Ut = rbind(cbind(Ut,zeros),cbind(Mt,zeros))
     ft = matrix(0, nrow=ages*2, ncol=ages*2)
     ft[1,1:ages] = f[,t] * birth_female
-    if(is.null(pi)){
+    if(pi_N_null_flag){
       A <- Ut[1:ages,1:ages] + ft[1:ages,1:ages]
       A_decomp = eigen(A)
       w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))

From 6eb6449b70811259acd432ebb2b239c54f6b75b0 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 28 Apr 2023 11:42:02 -0300
Subject: [PATCH 16/89] pi_N_null_flag

---
 R/kin_time_variant.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index 8e31031..1d83108 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -38,6 +38,7 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
   zeros        <- matrix(0, nrow=ages, ncol=ages)
 
   # age distribution at childborn
+  pi_N_null_flag <- FALSE
   if(is.null(pi)){
     if(is.null(n)){
       # create pi and fill it during the loop

From f45a1c0e0ecb392385b8c80f601dfafb44abcd1c Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 19 May 2023 12:58:38 -0300
Subject: [PATCH 17/89] preparing cran

---
 DESCRIPTION                             |   6 +-
 NAMESPACE                               |   2 +-
 R/aux_funs.R                            |  58 ++-----
 R/data.R                                |   9 ++
 R/kin.R                                 |  20 +++
 R/kin2sex.R                             |  23 +++
 R/kin_multi_stage.R                     | 191 ++++++++++++++----------
 R/kin_time_invariant_2sex.R             |   3 +
 R/kin_time_variant.R                    |  25 +++-
 R/kin_time_variant_2sex.R               |  34 +----
 README.Rmd                              |   4 +-
 README.md                               |  38 ++---
 data/demokin_codes.rda                  | Bin 0 -> 607 bytes
 dev/demokin_codes.R                     |  28 ++++
 man/demokin_codes.Rd                    |  13 +-
 man/kin2sex.Rd                          |   4 +
 man/kin_multi_stage.Rd                  |  11 +-
 man/output_period_cohort_combination.Rd |  29 ++--
 man/rename_kin.Rd                       |  12 +-
 man/timevarying_kin_2sex.Rd             |   5 +-
 tests/testthat/test-kin_multi_stage.R   |   3 +-
 vignettes/Reference_OneSex.Rmd          |  19 +--
 22 files changed, 318 insertions(+), 219 deletions(-)
 create mode 100644 data/demokin_codes.rda
 create mode 100644 dev/demokin_codes.R

diff --git a/DESCRIPTION b/DESCRIPTION
index 6abffee..fbb511d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: DemoKin
-Title: Estimate population kin counts.
+Title: Estimate population kin counts
 Description: Estimate population kin counts and its distribution by type, age and sex.
     The package implements one-sex and two-sex framework for studying living-death availability,
     with time varying rates or not, and multi-stage model.
@@ -7,7 +7,8 @@ Version: 1.0.0
 Authors@R: c(
     person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"),
     person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"),
-    person("Xi", "Song", email = "xisong@sas.upenn.edu", role = "ctb"))
+    person("Xi", "Song", email = "xisong@sas.upenn.edu", role = "ctb"),
+    person("Caswell", "Hal", email = "caswell@demogr.mpg.de", role = "ctb"))
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
@@ -23,7 +24,6 @@ Imports:
     dplyr,
     tidyr,
     purrr,
-    HMDHFDplus,
     progress,
     matrixcalc,
     Matrix,
diff --git a/NAMESPACE b/NAMESPACE
index 2d84ee7..72c7b74 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,7 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
-export(demokin_codes)
 export(kin)
 export(kin2sex)
 export(kin_multi_stage)
@@ -9,6 +8,7 @@ export(kin_time_invariant)
 export(kin_time_invariant_2sex)
 export(kin_time_variant)
 export(kin_time_variant_2sex)
+export(output_period_cohort_combination)
 export(plot_diagram)
 export(rename_kin)
 importFrom(magrittr,"%>%")
diff --git a/R/aux_funs.R b/R/aux_funs.R
index e8c745b..bead1b1 100644
--- a/R/aux_funs.R
+++ b/R/aux_funs.R
@@ -1,49 +1,15 @@
-
-#' print kin codes
-#' @description Print kin codes and labels
-#' @export
-demokin_codes <- function(){
-  codes <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
-  caswell_codes <- c("t", "v", "a", "b", "c", "h", "g", "d", "p", "q", "r", "s", "m", "n")
-  labels <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister")
-  data.frame(DemoKin = codes, Caswell = caswell_codes, Label = labels, row.names = NULL)
-}
-
 #' rename kin
-#' @description Rename kin labels depending consolidate some types
-#' @export
-rename_kin <- function(df, consolidate_column = "no"){
-
-  stopifnot("Argument 'consolidate_column' should be 'no' or a valid column name" = consolidate_column %in% c("no", colnames(df)))
-
-  if(consolidate_column == "no"){
-
-    relatives <- c("Cousins from older aunt", "Cousins from younger aunt", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces from older sister", "Nieces from younger sister", "Aunt older than mother", "Aunt younger than mother", "Older sister", "Younger sister")
-    names(relatives) <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
 
-  } else {
-
-    # Combine kin types irrespective of whether they come from older
-    # or younger sibling lines
-    consolidate_vec <- c("c", "c", "d", "gd", "ggd", "ggm", "gm", "m", "n", "n", "a", "a", "s", "s")
-    names(consolidate_vec) <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
-
-    # Rename kin types from codes to actual words
-    relatives <- c("Cousins", "Daughter", "Grand-daughter", "Great-grand-daughter", "Great-grandmother", "Grandmother", "Mother", "Nieces", "Aunt", "Sister")
-    names(relatives) <-  unique(consolidate_vec)
-
-    df <- as.data.frame(df)
-    df$count <- df[ , consolidate_column]
-
-    df <-
-      df %>%
-      dplyr::mutate(kin = consolidate_vec[kin]) %>%
-      dplyr::group_by(age_focal, kin) %>%
-      dplyr::summarise(count = sum(count)) %>%
-      dplyr::ungroup()
-
-
-  }
-  df$kin <- relatives[df$kin]
-  df
+#' @description Add kin labels depending the sex
+#' @details See table `demokin_codes` to know label options.
+#' @param df data.frame. A data frame with variable `kin` with `DemoKin` codes to be labelled.
+#' @param sex character. "f" for female, "m" for male or "2sex" for both sex naming.
+#' @export
+rename_kin <- function(df, sex = "f"){
+  if(sex == "f") demokin_codes_sex <- DemoKin::demokin_codes[,c("DemoKin", "Labels_female")]
+  if(sex == "m") demokin_codes_sex <- DemoKin::demokin_codes[,c("DemoKin", "Labels_male")]
+  if(sex == "2sex") demokin_codes_sex <- DemoKin::demokin_codes[,c("DemoKin", "Labels_2sex")]
+  colnames(demokin_codes_sex) <- c("kin", "kin_label")
+  df %>%
+    dplyr::left_join(demokin_codes_sex)
 }
diff --git a/R/data.R b/R/data.R
index b11636a..43be162 100644
--- a/R/data.R
+++ b/R/data.R
@@ -149,3 +149,12 @@
 #' @source
 #'   Caswell (2022)
 "fra_surv_sex"
+
+#' DemoKin codes, Caswell (2020) codes, and useful labels.
+#'
+#' DemoKin codes, Caswell (2020) codes, and useful labels.
+#' @docType data
+#' @format
+#' A data.frame with codes and labels for distinction between kin types.
+
+"demokin_codes"
diff --git a/R/kin.R b/R/kin.R
index 73f74be..b5a898b 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -52,6 +52,9 @@ kin <- function(p = NULL, f = NULL,
                 U = lifecycle::deprecated())
   {
 
+  # global vars
+  living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+
   # changed arguments
   if (lifecycle::is_present(stable)) {
     lifecycle::deprecate_warn("0.0.0.9000", "kin(stable)", details = "Used time_invariant")
@@ -64,9 +67,15 @@ kin <- function(p = NULL, f = NULL,
 
   # kin to return
   all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
+  output_kin_asked <- output_kin
   if(is.null(output_kin)){
     output_kin <- all_possible_kin
   }else{
+    if("s" %in% output_kin) output_kin <- c(output_kin, "os", "ys")
+    if("c" %in% output_kin) output_kin <- c(output_kin, "coa", "cya")
+    if("a" %in% output_kin) output_kin <- c(output_kin, "oa", "ya")
+    if("n" %in% output_kin) output_kin <- c(output_kin, "nos", "nys")
+    output_kin <- output_kin[!output_kin %in% c("s", "c", "a", "n")]
     output_kin <- match.arg(tolower(output_kin), all_possible_kin, several.ok = TRUE)
   }
 
@@ -91,6 +100,17 @@ kin <- function(p = NULL, f = NULL,
       message(paste0("Assuming stable population before ", min(years_data), "."))
   }
 
+  # re-group if grouped type is asked
+  if(length(output_kin_asked)!=length(output_kin)){
+    if("s" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("os", "ys")]   <- "s"
+    if("c" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("coa", "cya")] <- "c"
+    if("a" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("oa", "ya")]   <- "a"
+    if("n" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("nos", "nys")] <- "n"
+    kin_full <- kin_full %>%
+      dplyr::summarise(living = sum(living), dead = sum(dead),
+                       .by = c(kin, age_kin, age_focal, cohort, year))
+  }
+
   # select period/cohort
   if(!is.null(output_cohort)){
     agrupar <- "cohort"
diff --git a/R/kin2sex.R b/R/kin2sex.R
index 08da0c6..cd044cb 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -38,6 +38,10 @@
 #' @examples
 #' \dontrun{
 #' # Kin expected count by relative sex for a French female based on 2012 rates.
+#' fra_fert_f <- fra_asfr_sex[,"ff"]
+#' fra_fert_m <- fra_asfr_sex[,"fm"]
+#' fra_surv_f <- fra_surv_sex[,"pf"]
+#' fra_surv_m <- fra_surv_sex[,"pm"]
 #' fra_2012 <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m)
 #' head(fra_2012)
 #'}
@@ -52,14 +56,22 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
                  output_cohort = NULL, output_period = NULL, output_kin=NULL)
   {
 
+  # global vars
+  living<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-sex_kin<-age_kin<-dead<-NULL
   age <- as.integer(rownames(pf))
   years_data <- as.integer(colnames(pf))
 
   # kin to return
   all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
+  output_kin_asked <- output_kin
   if(is.null(output_kin)){
     output_kin <- all_possible_kin
   }else{
+    if("s" %in% output_kin) output_kin <- c(output_kin, "os", "ys")
+    if("c" %in% output_kin) output_kin <- c(output_kin, "coa", "cya")
+    if("a" %in% output_kin) output_kin <- c(output_kin, "oa", "ya")
+    if("n" %in% output_kin) output_kin <- c(output_kin, "nos", "nys")
+    output_kin <- output_kin[!output_kin %in% c("s", "c", "a", "n")]
     output_kin <- match.arg(tolower(output_kin), all_possible_kin, several.ok = TRUE)
   }
 
@@ -94,6 +106,17 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   # reorder
   kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead)
 
+  # re-group if grouped type is asked
+  if(length(output_kin_asked)!=length(output_kin)){
+    if("s" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("os", "ys")]   <- "s"
+    if("c" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("coa", "cya")] <- "c"
+    if("a" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("oa", "ya")]   <- "a"
+    if("n" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("nos", "nys")] <- "n"
+    kin_full <- kin_full %>%
+      dplyr::summarise(living = sum(living), dead = sum(dead),
+                       .by = c(kin, age_kin, age_focal, sex_kin, cohort, year))
+  }
+
   # summary
   # select period/cohort
   if(!is.null(output_cohort)){
diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R
index 7129134..bea168f 100644
--- a/R/kin_multi_stage.R
+++ b/R/kin_multi_stage.R
@@ -2,12 +2,13 @@
 
 #' @description Implementation of age-stage kin estimates (multi-state) by Caswell (2020). Stages are implied in length of input lists.
 
-#' @param U list. age elemnts with column-stochastic transition matrix with dimension for the state space, conditional on survival.
-#' @param f matrix. state-specific fertility (age in rows and states in columns).
-#' @param D matrix. survival probabilities by state (age in rows and states in columns)
-#' @param H matrix. assigns the offspring of individuals in some stage to the appropriate age class with 1 (age in rows and states in columns).
+#' @param U list. age elements with column-stochastic transition matrix with dimension for the state space, conditional on survival.
+#' @param f matrix. state-specific fertility (age in rows and states in columns). Is accepted also a list with for each age-class.
+#' @param D matrix. survival probabilities by state (age in rows and states in columns). Is accepted also a list for each state with survival matrices.
+#' @param H matrix. assigns the offspring of individuals in some stage to the appropriate age class (age in rows and states in columns). Is accepted also a list with a matrix for each state.
 #' @param output_kin character. kin to return. For example "m" for mother, "d" for daughter. See the `vignette` for all kin types.
 #' @param birth_female numeric. Female portion at birth.
+#' @param parity logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default `TRUE`.
 #' @param list_output logical. Results as a list. Default `FALSE`.
 
 #' @return A data frame with focal´s age, related ages and type of kin
@@ -16,96 +17,109 @@
 #'
 
 kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
-                               birth_female = 1/2.04,
-                               output_kin = NULL,
-                               list_output = FALSE){
+                            birth_female = 1/2.04,
+                            output_kin = NULL,
+                            parity = FALSE,
+                            list_output = FALSE){
 
+  # mandatory U as a list
   if(!is.list(U)) stop("U must be a list with age length of elements, and stage transitiotn matrix for each one.")
 
-  # stages and ages
+  # stages and age-classes
   s <- ncol(U[[1]])
   ages <- length(U)
   age <- (1:ages)-1
 
-  # build matrix structure from data.frame input
-  H <- purrr::map(colnames(D), function(Y){
-    Ht = matrix(0, nrow=ages, ncol=ages)
-    Ht[1,] <- 1
-    Ht
-  })
-  D <- purrr::map(colnames(D), function(Y){
+  # build H if it is not already a list
+  if(!is.list(H)){
+    H <- purrr::map(1:s, function(Y){
+      Ht = matrix(0, nrow=ages, ncol=ages)
+      Ht[1,] <- 1
+      Ht
+    })
+  }
+
+  # build D if it is not already a list
+  if(!is.list(D)){
+    D <- purrr::map(1:s, function(Y){
       X <- D[,Y]
       Dt = matrix(0, nrow=ages, ncol=ages)
       Dt[row(Dt)-1 == col(Dt)] <- X[-ages]
       Dt[ages, ages] = X[ages]
       Dt
     })
-  f <- purrr::map(1:ages, function(Y){
-    X <- f[Y,]
-    ft = matrix(0, nrow=s, ncol=s)
-    ft[1,] <- X
-    ft
-  })
-
-  # build block matrix
+  }
+
+  # build f if it is not already a list
+  if(!is.list(f)){
+    f <- purrr::map(1:ages, function(Y){
+      X <- f[Y,]
+      ft = matrix(0, nrow=s, ncol=s)
+      ft[1,] <- X
+      ft
+    })
+  }
+
+  # build block matrices
   bbU <- Matrix::bdiag(U)
   bbF <- Matrix::bdiag(f) * birth_female
   bbD <- Matrix::bdiag(D)
   bbH <- Matrix::bdiag(H)
 
-  # rearrange with conmutation matrix
+  # order transitions: first state within age, then age given state
   K <- matrixcalc::commutation.matrix(s, ages)
   Ut <- t(K) %*% bbD %*% K %*% bbU
   ft <-  t(K) %*% bbH %*% K %*% bbF
+
+  # focal transition but conditioned to survive
   Gt <-  Ut%*% MASS::ginv(diag(colSums(as.matrix(Ut))))
 
-  # stable distribution mothers: age x stage
+  # stable distribution of mothers
   At <- Ut + ft
   A_decomp <- eigen(At)
   lambda <- as.double(A_decomp$values[1])
   wt <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
   pi <- wt*At[1,]/sum(wt*At[1,])
 
-  # marginal mothers age
-  Iom <- diag(1,ages, ages);
-  ones <- t(rep(1,s))
+  # useful vectors and matrices
+  ones   <- t(rep(1,s))
   onesom <- t(rep(1,s*ages))
-  piage <- kronecker(Iom,ones) %*% pi
-
-  # momarray is an array with pit in each column
-  momarray <- pi %*% matrix(1,1,ages)
-  Iom = diag(1, ages)
-  Is = diag(1, s)
-  Isom = diag(1, s*ages)
-  zsom = matrix(0, s*ages, s*ages)
-  Z=Is;
-  Z[1,1]=0;
-  for(i in 1:ages){
-    # imom = 1
-    E <- Iom[,i] %*% t(Iom[i,]); # al cuadrado?
-    momarray[,i] <- kronecker(E,Z) %*% momarray[,i]
-  }
-  # re-scale
-  momarray <- momarray %*% MASS::ginv(diag(colSums(momarray)))
-
-  # considering deaths
-  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,ages*s*2,ages)
-  phi[1,1] = 1
+  onesa  <- t(rep(1,ages))
+  Iom    <- diag(1, ages)
+  Is     <- diag(1, s)
+  Isom   <- diag(1, s*ages)
+  zsom   <- matrix(0, s*ages, s*ages)
+
+  # momarray is an array with pi in each column
+  piage  <- kronecker(Iom,ones) %*% pi
+  momarray <- pi %*% onesa
+
+  # considering deaths (no cumulated): reacreate block struct matrices
+  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,ages * s * 2, ages)
   Mtt <- diag(as.numeric(onesom - onesom %*% Ut))
   Utt <- rbind(cbind(Ut,zsom), cbind(Mtt,Isom)) %>% as.matrix()
   ftt <- rbind(cbind(ft,zsom), cbind(zsom,zsom)) %>% as.matrix()
   Gtt <- rbind(cbind(Gt,zsom), cbind(zsom,zsom)) %>% as.matrix()
   sages <- 1:(ages*s)
 
-  # no considering deaths
-    # phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,ages*s,ages)
-    # phi[1,1] = 1
-    # Utt = Ut %>% as.matrix()
-    # ftt = ft %>% as.matrix()
-    # Gtt = Gt %>% as.matrix()
+  # if parity: restriction to no initial mothers with 0 parity
+  if(parity){
+    Z=Is
+    Z[1,1]=0
+    for(i in 1:ages){
+      E <- Iom[,i] %*% t(Iom[i,])
+      momarray[,i] <- kronecker(E,Z) %*% momarray[,i]
+    }
+    # re-scale
+    momarray <- momarray %*% MASS::ginv(diag(colSums(momarray)))
+    # no 0 parity mothers: (momarray %*% piage)[seq(1,600,6)]
+    m[sages,1] = momarray %*% piage
+  }else{
+    m[sages,1] = pi
+  }
 
   # focal´s trip
-  m[sages,1] = momarray %*% piage;
+  phi[1,1] = 1
   for(i in 1:(ages-1)){
     phi[,i+1] = Gtt %*% phi[,i]
     d[,i+1]   = Utt %*% d[,i] + ftt %*% phi[,i]
@@ -145,7 +159,8 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
   }
 
   # get results
-  kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
+  kin_list <- list(focal = phi,
+                   d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
                    nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
 
   # only selected kin
@@ -153,34 +168,56 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
     kin_list <- kin_list %>% purrr::keep(names(.) %in% output_kin)
   }
 
-  # as data.frame
-  kin <- purrr::map2(kin_list, names(kin_list),
-              function(x,y){
-                out <- as.data.frame(x)
-                colnames(out) <- age
-                out %>%
-                  dplyr::mutate(kin = y,
-                         age_kin = rep(sort(rep(age,s)),2),
-                         stage_kin = rep(rep(1:s,ages),2),
-                         alive = c(rep("living",s*ages),rep("dead",s*ages))
-                         # age_kin = sort(rep(age,s)),
-                         # stage_kin = rep(1:s,ages),
-                         # alive = c(rep("yes",s*ages))
-                         ) %>%
-                  tidyr::pivot_longer(c(-age_kin, -stage_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>%
-                  dplyr::mutate(age_focal = as.integer(age_focal)) %>%
-                  tidyr::pivot_wider(names_from = alive, values_from = count)
-              }) %>%
+  # kin_full as data.frame
+  kin_full <- purrr::map2(kin_list, names(kin_list),
+                          function(x,y){
+                            out <- as.data.frame(x)
+                            colnames(out) <- age
+                            out %>%
+                              dplyr::mutate(kin = y,
+                                            age_kin = rep(sort(rep(age,s)),2),
+                                            stage_kin = rep(rep(1:s,ages),2),
+                                            alive = c(rep("living",s*ages),rep("dead",s*ages))) %>%
+                              tidyr::pivot_longer(c(-age_kin, -stage_kin, -kin, -alive), names_to = "age_focal", values_to = "count") %>%
+                              dplyr::mutate(age_focal = as.integer(age_focal)) %>%
+                              tidyr::pivot_wider(names_from = alive, values_from = count)
+                          }) %>%
     purrr::reduce(rbind)
 
   # results as list?
   if(list_output) {
     out <- kin_list
   }else{
-    out <- kin
+    out <- kin_full
   }
 
+  # end
   return(out)
 }
 
-
+# function to create lists for the parity case given a set of coniditonal rates and survival probabilities with stages in columns and ages in rows
+make_mulstistate_parity_matrices <- function(f_parity, p_parity, birth_female=.5){
+  ages <- nrow(f_parity)
+  stages <- ncol(f_parity) + 1
+  F_list <- U_list <- D_list <- H_list <- list()
+  for(x in 1:ages){
+    cond_probs <- as.numeric(f_parity[x,]/(1+f_parity[x,]/2))
+    U_age <- matrix(0,stages,stages)
+    diag(U_age) <-  c(1 - cond_probs, 1)
+    U_age[row(U_age)-1==col(U_age)] <- cond_probs
+    U_list[[x]] <- U_age
+    F_age <- matrix(0,stages,stages)
+    F_age[1,] <- c(cond_probs,cond_probs[stages-1])*birth_female
+    F_list[[x]] <- F_age
+  }
+  p_parity$px_last <- p_parity[,stages-1]
+  for(s in 1:stages){
+    H_age <- D_age <- matrix(0,ages,ages)
+    H_age[1,] <- 1
+    D_age[row(D_age)-1==col(D_age)] <- p_parity[-ages,s]
+    D_age[stages, stages] <- p_parity[ages,s]
+    H_list[[s]] <- H_age
+    D_list[[s]] <- D_age
+  }
+  return(list(U = U_list, F. = F_list, H = H_list, D = D_list))
+}
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index 7e8fe16..e8e4c4b 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -27,6 +27,9 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
                                     output_kin = NULL,
                                     list_output = FALSE){
 
+  # global vars
+  living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+
   # same input length
   if(!all(length(pf)==length(pm), length(pf)==length(ff), length(pf)==length(fm))) stop("Lengths of p's and f's should be the same")
 
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index 1d83108..55adffe 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -20,15 +20,18 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
                             output_cohort = NULL, output_period = NULL, output_kin = NULL,
                             birth_female = 1/2.04, list_output = FALSE){
 
+  # global vars
+  living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+
   # check input
   if(is.null(p) | is.null(f)) stop("You need values on p and f.")
 
   # diff years
-  if(!any(as.integer(colnames(p)) == as.integer(colnames(f)))) stop("Data should be from same years.")
+  if(!any(as.integer(colnames(p)) == as.integer(colnames(f)))) stop("Make sure that p and f are matrices and have the same column names.")
 
   # data should be from same interval years
   years_data <- as.integer(colnames(p))
-  if(var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again")
+  if(var(diff(years_data))!=0) stop("The years given as column names in the p and f matrices must be equally spaced.")
 
   # utils
   age          <- 0:(nrow(p)-1)
@@ -104,6 +107,7 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
     purrr::map(~ .[selected_kin_position])
 
   # long format
+  cat("Preparing output...")
   kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
     X <- purrr::map2(X, names(X), function(x,y){
@@ -123,7 +127,6 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
     X[X$age_focal %in% out_selected$age[out_selected$year==as.integer(Y)],] %>%
       data.table::dcast(year + kin + age_kin + age_focal + cohort ~ alive, value.var = "count")
     }) %>% data.table::rbindlist()
-  pb$tick()
 
   # results as list?
   if(list_output) {
@@ -193,10 +196,17 @@ timevarying_kin<- function(Ut, ft, pit, ages, pkin){
   return(kin_list)
 }
 
-#' defince apc combination to return
+#' APC combination to return
 
-#' @description defince apc to return.
-#'
+#' @description define APC combination to return in `kin` and `kin2sex`.
+#' @details Because returning all period and cohort data from a huge time-series would be hard memory consuming,
+#' this function is an auxiliary one to deal with selection from inputs `output_cohort` and `output_period`.
+#' @param output_cohort integer. A vector with selected calendar years.
+#' @param output_period integer. A vector with selected cohort years.
+#' @param age integer. A vector with ages from the kinship network to be filtered.
+#' @param years_data integer. A vector with years from the time-varying kinship network to be filtered.
+#' @return data.frame with years and ages to filter in `kin` and `kin_2sex` functions.
+#' @export
 output_period_cohort_combination <- function(output_cohort = NULL, output_period = NULL, age = NULL, years_data = NULL){
 
   # no specific
@@ -212,10 +222,11 @@ output_period_cohort_combination <- function(output_cohort = NULL, output_period
                                               unlist(use.names = F))
   }else{selected_cohorts_year_age <- c()}
 
-  # period year combination
+  # period combination
   if(!is.null(output_period)){selected_years_age <- expand.grid(age, output_period) %>% dplyr::rename(age=1,year=2)
   }else{selected_years_age <- c()}
 
   # end
   return(dplyr::bind_rows(selected_years_age,selected_cohorts_year_age) %>% dplyr::distinct())
 }
+
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index f0b1479..cd9d560 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -30,6 +30,9 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
                                    output_cohort = NULL, output_period = NULL, output_kin = NULL,
                                    list_output = FALSE){
 
+  # global vars
+  living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+
   # same input length
   if(!all(dim(pf) == dim(pm), dim(pf) == dim(ff), dim(pf) == dim(fm))) stop("Dimension of P's and F's should be the same")
 
@@ -145,6 +148,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
     purrr::keep(names(.) %in% as.character(unique(out_selected$year))) %>%
     purrr::map(~ .[selected_kin_position])
   # long format
+  cat("Preparing output...")
   kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
     X <- purrr::map2(X, names(X), function(x,y){
@@ -165,7 +169,6 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
     X <- X[X$age_focal %in% out_selected$age[out_selected$year==as.integer(Y)],]
     X <- data.table::dcast(X, year + kin + sex_kin + age_kin + age_focal + cohort ~ alive, value.var = "count", fun.aggregate = sum)
   }) %>% data.table::rbindlist()
-  pb$tick()
 
   # results as list?
   if(list_output) {
@@ -184,7 +187,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
 #' @param Ft numeric. A matrix of age-specific fertility rates.
 #' @param Ft_star numeric. Ft but for female fertility.
 #' @param pit numeric. A matrix with distribution of childbearing.
-#' sex_focal
+#' @param sex_focal character. "f" for female or "m" for male.
 #' @param ages numeric.
 #' @param pkin numeric. A list with kin count distribution in previous year.
 #
@@ -242,30 +245,3 @@ timevarying_kin_2sex<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
 
   return(kin_list)
 }
-
-#' APC combination to return
-
-#' @description define apc combination to return in `kin` and `kin2sex`.
-#'
-output_period_cohort_combination <- function(output_cohort = NULL, output_period = NULL, age = NULL, years_data = NULL){
-
-  # no specific
-  if(is.null(output_period) & is.null(output_cohort)){
-    message("No specific output was set. Return all period data.")
-    output_period <- years_data
-  }
-
-  # cohort combination
-  if(!is.null(output_cohort)){
-    selected_cohorts_year_age <- data.frame(age = rep(age,length(output_cohort)),
-                                            year = purrr::map(output_cohort,.f = ~.x+age) %>%
-                                              unlist(use.names = F))
-  }else{selected_cohorts_year_age <- c()}
-
-  # period year combination
-  if(!is.null(output_period)){selected_years_age <- expand.grid(age, output_period) %>% dplyr::rename(age=1,year=2)
-  }else{selected_years_age <- c()}
-
-  # end
-  return(dplyr::bind_rows(selected_years_age,selected_cohorts_year_age) %>% dplyr::distinct())
-}
diff --git a/README.Rmd b/README.Rmd
index d2426f1..721e1ed 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -42,7 +42,7 @@ devtools::install_github("IvanWilli/DemoKin")
 
 ## Usage
 
-Consider an average Swedish woman called 'Focal'. For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life (the 'time-invariant' assumption in Caswell [2019]). 
+Consider an average Swedish woman called 'Focal'. For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life (the 'time-invariant' assumption in Caswell (2019)). 
 
 We then ask:
 
@@ -73,7 +73,7 @@ plot_diagram(kin_total, rounding = 2)
 Relatives are identified by a unique code:
 
 ```{r, fig.height=6, fig.width=8, echo=FALSE}
-kable(DemoKin::demokin_codes()[-2])
+kable(DemoKin::demokin_codes[,c(1,3)])
 ```
 
 ## Vignette
diff --git a/README.md b/README.md
index a8badbf..e5d467d 100644
--- a/README.md
+++ b/README.md
@@ -35,7 +35,7 @@ devtools::install_github("IvanWilli/DemoKin")
 Consider an average Swedish woman called ‘Focal’. For this exercise, we
 assume a female closed population in which everyone experiences the
 Swedish 2015 mortality and fertility rates at each age throughout their
-life (the ‘time-invariant’ assumption in Caswell \[2019\]).
+life (the ‘time-invariant’ assumption in Caswell (2019)).
 
 We then ask:
 
@@ -71,22 +71,26 @@ plot_diagram(kin_total, rounding = 2)
 
 Relatives are identified by a unique code:
 
-| DemoKin | Label                      |
-|:--------|:---------------------------|
-| coa     | Cousins from older aunt    |
-| cya     | Cousins from younger aunt  |
-| d       | Daughter                   |
-| gd      | Grand-daughter             |
-| ggd     | Great-grand-daughter       |
-| ggm     | Great-grandmother          |
-| gm      | Grandmother                |
-| m       | Mother                     |
-| nos     | Nieces from older sister   |
-| nys     | Nieces from younger sister |
-| oa      | Aunt older than mother     |
-| ya      | Aunt younger than mother   |
-| os      | Older sister               |
-| ys      | Younger sister             |
+| DemoKin | Labels_female               |
+|:--------|:----------------------------|
+| coa     | Cousins from older aunts    |
+| cya     | Cousins from younger aunts  |
+| c       | Cousins                     |
+| d       | Daughters                   |
+| gd      | Grand-daughters             |
+| ggd     | Great-grand-daughters       |
+| ggm     | Great-grandmothers          |
+| gm      | Grandmothers                |
+| m       | Mother                      |
+| nos     | Nieces from older sisters   |
+| nys     | Nieces from younger sisters |
+| n       | Nieces                      |
+| oa      | Aunts older than mother     |
+| ya      | Aunts younger than mother   |
+| a       | Aunts                       |
+| os      | Older sisters               |
+| ys      | Younger sisters             |
+| s       | Sisters                     |
 
 ## Vignette
 
diff --git a/data/demokin_codes.rda b/data/demokin_codes.rda
new file mode 100644
index 0000000000000000000000000000000000000000..3859ab313ac97b245ae997bc2c4d20df6deaf116
GIT binary patch
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zNtPsIvWwYJ<jr5~Pf!=wo9x{LnMra#&hDOj&s_HF@cv!vzGYdKV;$6N%W<V?xsz`<
z{VPjGq+?qLR#Sf4K27-dh&_1Rr$R0`*&yZ9k<YOLIUXmuxdzkr!0?q(3&3&$-AwX4
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zode1wMO~1=EMI=$OA#^Ao2NYK@z|$nk1SaRWu2k?nlD*kAQ}px+~$xhgD|5h1dc{2
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zwG&P79MRGB(&D;Qb5fQH$LcpkYfN`c7a?7tLS~rAYXpH(3{XRXpg)ssrMh64#hC&i
z$>ut<`b2Nmv)d|aooO|jqL)~<^L?W>K+0U~pSZz)CA^Dfal`^+?z}LH{gkrGxiga|
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t0jEN+<&%tD&QnRqfHzaVx&)%_%{BH*zU1ZbTYmm*o`1DPph(FF002*=Cr<zX

literal 0
HcmV?d00001

diff --git a/dev/demokin_codes.R b/dev/demokin_codes.R
new file mode 100644
index 0000000..69a545a
--- /dev/null
+++ b/dev/demokin_codes.R
@@ -0,0 +1,28 @@
+#' demokin kin codes
+
+codes <- c("coa", "cya", 'c', "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "n", "oa", "ya", "a","os", "ys", "s")
+caswell_codes <- c("t", "v", NA, "a", "b", "c", "h", "g", "d", "p", "q", NA, "r", "s", NA,"m", "n",NA)
+labels_female <- c("Cousins from older aunts", "Cousins from younger aunts", "Cousins",
+                   "Daughters", "Grand-daughters",
+                   "Great-grand-daughters", "Great-grandmothers", "Grandmothers", "Mother",
+                   "Nieces from older sisters", "Nieces from younger sisters",  "Nieces",
+                   "Aunts older than mother", "Aunts younger than mother",  "Aunts",
+                   "Older sisters", "Younger sisters", "Sisters")
+labels_male <- c("Cousins from older uncles", "Cousins from younger uncles", "Cousins",
+                 "Brothers", "Grand-sons",
+                 "Great-grand-sons", "Great-grandfathers", "Grandfathers", "Father",
+                 "Nephews from older brothers", "Nephews from younger brothers", "Nephews",
+                 "Uncles older than fathers", "Uncles younger than father", "Uncles",
+                 "Older brothers", "Younger brothers", "Brothers")
+labels_2sex <- c("Cousins from older aunts/uncles", "Cousins from younger aunts/uncles", "Cousins",
+                 "Siblings", "Grand-childrens",
+                 "Great-grand-childrens", "Great-grandfparents", "Grandparents", "Parents",
+                 "Niblings from older siblings", "Niblings from younger siblings", "Niblings",
+                 "Aunts/Uncles older than parents", "Aunts/Uncles younger than parents", "Aunts/Uncles",
+                 "Older siblings", "Younger siblings", "Siblings")
+demokin_codes <- data.frame(DemoKin = codes, Caswell = caswell_codes,
+           Labels_female = labels_female,
+           Labels_male   = labels_male,
+           Labels_2sex   = labels_2sex,
+           row.names = NULL)
+# save(demokin_codes, file = "data/demokin_codes.rda")
diff --git a/man/demokin_codes.Rd b/man/demokin_codes.Rd
index deb1b04..6f1e241 100644
--- a/man/demokin_codes.Rd
+++ b/man/demokin_codes.Rd
@@ -1,11 +1,16 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/aux_funs.R
+% Please edit documentation in R/data.R
+\docType{data}
 \name{demokin_codes}
 \alias{demokin_codes}
-\title{print kin codes}
+\title{DemoKin codes, Caswell (2020) codes, and useful labels.}
+\format{
+A data.frame with codes and labels for distinction between kin types.
+}
 \usage{
-demokin_codes()
+demokin_codes
 }
 \description{
-Print kin codes and labels
+DemoKin codes, Caswell (2020) codes, and useful labels.
 }
+\keyword{datasets}
diff --git a/man/kin2sex.Rd b/man/kin2sex.Rd
index d91b032..cde73cd 100644
--- a/man/kin2sex.Rd
+++ b/man/kin2sex.Rd
@@ -79,6 +79,10 @@ See Caswell (2022) for details on formulas.
 \examples{
 \dontrun{
 # Kin expected count by relative sex for a French female based on 2012 rates.
+fra_fert_f <- fra_asfr_sex[,"ff"]
+fra_fert_m <- fra_asfr_sex[,"fm"]
+fra_surv_f <- fra_surv_sex[,"pf"]
+fra_surv_m <- fra_surv_sex[,"pm"]
 fra_2012 <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m)
 head(fra_2012)
 }
diff --git a/man/kin_multi_stage.Rd b/man/kin_multi_stage.Rd
index 12fa213..7a0830d 100644
--- a/man/kin_multi_stage.Rd
+++ b/man/kin_multi_stage.Rd
@@ -11,22 +11,25 @@ kin_multi_stage(
   H = NULL,
   birth_female = 1/2.04,
   output_kin = NULL,
+  parity = FALSE,
   list_output = FALSE
 )
 }
 \arguments{
-\item{U}{list. age elemnts with column-stochastic transition matrix with dimension for the state space, conditional on survival.}
+\item{U}{list. age elements with column-stochastic transition matrix with dimension for the state space, conditional on survival.}
 
-\item{f}{matrix. state-specific fertility (age in rows and states in columns).}
+\item{f}{matrix. state-specific fertility (age in rows and states in columns). Is accepted also a list with for each age-class.}
 
-\item{D}{matrix. survival probabilities by state (age in rows and states in columns)}
+\item{D}{matrix. survival probabilities by state (age in rows and states in columns). Is accepted also a list for each state with survival matrices.}
 
-\item{H}{matrix. assigns the offspring of individuals in some stage to the appropriate age class with 1 (age in rows and states in columns).}
+\item{H}{matrix. assigns the offspring of individuals in some stage to the appropriate age class (age in rows and states in columns). Is accepted also a list with a matrix for each state.}
 
 \item{birth_female}{numeric. Female portion at birth.}
 
 \item{output_kin}{character. kin to return. For example "m" for mother, "d" for daughter. See the \code{vignette} for all kin types.}
 
+\item{parity}{logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default \code{TRUE}.}
+
 \item{list_output}{logical. Results as a list. Default \code{FALSE}.}
 }
 \value{
diff --git a/man/output_period_cohort_combination.Rd b/man/output_period_cohort_combination.Rd
index c96a235..62fc02e 100644
--- a/man/output_period_cohort_combination.Rd
+++ b/man/output_period_cohort_combination.Rd
@@ -1,8 +1,8 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_time_variant.R, R/kin_time_variant_2sex.R
+% Please edit documentation in R/kin_time_variant.R
 \name{output_period_cohort_combination}
 \alias{output_period_cohort_combination}
-\title{defince apc combination to return}
+\title{APC combination to return}
 \usage{
 output_period_cohort_combination(
   output_cohort = NULL,
@@ -10,16 +10,23 @@ output_period_cohort_combination(
   age = NULL,
   years_data = NULL
 )
+}
+\arguments{
+\item{output_cohort}{integer. A vector with selected calendar years.}
 
-output_period_cohort_combination(
-  output_cohort = NULL,
-  output_period = NULL,
-  age = NULL,
-  years_data = NULL
-)
+\item{output_period}{integer. A vector with selected cohort years.}
+
+\item{age}{integer. A vector with ages from the kinship network to be filtered.}
+
+\item{years_data}{integer. A vector with years from the time-varying kinship network to be filtered.}
+}
+\value{
+data.frame with years and ages to filter in \code{kin} and \code{kin_2sex} functions.
 }
 \description{
-defince apc to return.
-
-define apc combination to return in \code{kin} and \code{kin2sex}.
+define APC combination to return in \code{kin} and \code{kin2sex}.
+}
+\details{
+Because returning all period and cohort data from a huge time-series would be hard memory consuming,
+this function is an auxiliary one to deal with selection from inputs \code{output_cohort} and \code{output_period}.
 }
diff --git a/man/rename_kin.Rd b/man/rename_kin.Rd
index 023cf99..ea52394 100644
--- a/man/rename_kin.Rd
+++ b/man/rename_kin.Rd
@@ -4,8 +4,16 @@
 \alias{rename_kin}
 \title{rename kin}
 \usage{
-rename_kin(df, consolidate_column = "no")
+rename_kin(df, sex = "f")
+}
+\arguments{
+\item{df}{data.frame. A data frame with variable \code{kin} with \code{DemoKin} codes to be labelled.}
+
+\item{sex}{character. "f" for female, "m" for male or "2sex" for both sex naming.}
 }
 \description{
-Rename kin labels depending consolidate some types
+Add kin labels depending the sex
+}
+\details{
+See table \code{demokin_codes} to know label options.
 }
diff --git a/man/timevarying_kin_2sex.Rd b/man/timevarying_kin_2sex.Rd
index d6dccf2..ba4f03e 100644
--- a/man/timevarying_kin_2sex.Rd
+++ b/man/timevarying_kin_2sex.Rd
@@ -13,8 +13,9 @@ timevarying_kin_2sex(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
 
 \item{Ft_star}{numeric. Ft but for female fertility.}
 
-\item{pit}{numeric. A matrix with distribution of childbearing.
-sex_focal}
+\item{pit}{numeric. A matrix with distribution of childbearing.}
+
+\item{sex_focal}{character. "f" for female or "m" for male.}
 
 \item{ages}{numeric.}
 
diff --git a/tests/testthat/test-kin_multi_stage.R b/tests/testthat/test-kin_multi_stage.R
index 2e3d58d..d7a845b 100644
--- a/tests/testthat/test-kin_multi_stage.R
+++ b/tests/testthat/test-kin_multi_stage.R
@@ -4,7 +4,8 @@ test_that("same output in multi_stage (caswell 2020)", {
   demokin_svk1990_caswell2020 <- kin_multi_stage(U = svk_Uxs,
                                                  f = svk_fxs,
                                                  D = svk_pxs,
-                                                 H = svk_Hxs, birth_female=1, list_output = TRUE)
+                                                 H = svk_Hxs, birth_female=1,
+                                                 list_output = TRUE, parity = TRUE)
   expect_equal(demokin_svk1990_caswell2020$d[1:(110*6),], kin_svk1990_caswell2020$d)
   expect_equal(demokin_svk1990_caswell2020$gd[1:(110*6),], kin_svk1990_caswell2020$gd)
   expect_equal(demokin_svk1990_caswell2020$ggd[1:(110*6),], kin_svk1990_caswell2020$ggd)
diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
index 1259f8b..eb93a02 100644
--- a/vignettes/Reference_OneSex.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -84,7 +84,7 @@ Here, each relative type is identified by a unique code. Note that `DemoKin` use
 ```{r, fig.height=6, fig.width=8, echo=FALSE}
 library(knitr)
 
-DemoKin::demokin_codes() %>% 
+demokin_codes %>% 
   kable
 ```
 
@@ -92,7 +92,6 @@ We can also visualize the age distribution of relatives when Focal is 35 years o
 
 ```{r, fig.height=6, fig.width=8}
 swe_2015[["kin_full"]] %>%
-  DemoKin::rename_kin() %>% 
   filter(age_focal == 35) %>% 
   ggplot() +
   geom_line(aes(age_kin, living))  +
@@ -110,7 +109,6 @@ The `kin` function also includes a summary output with the count of living kin,
 
 ```{r, fig.height=6, fig.width=8}
 swe_2015[["kin_summary"]] %>% 
-  DemoKin::rename_kin() %>% 
   filter(age_focal == 35) %>% 
   select(kin, count_living, mean_age, sd_age) %>% 
   mutate_if(is.numeric, round, 2) %>% 
@@ -145,7 +143,6 @@ swe_time_varying <-
     )
 
 swe_time_varying$kin_summary %>%
-  DemoKin::rename_kin() %>% 
   ggplot(aes(age_focal,count_living,color=factor(cohort))) +
   scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+
   geom_line(color = 1)+
@@ -165,7 +162,6 @@ The function `kin` also includes information on the number of relatives lost by
 
 ```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE}
 swe_time_varying$kin_summary %>%
-  DemoKin::rename_kin() %>% 
   ggplot() +
   geom_line(aes(age_focal, count_cum_dead)) +
   labs(y = "Expected number of deceased relatives") +
@@ -177,7 +173,6 @@ Given these population-level measures, we can compute Focal's the mean age at th
 
 ```{r}
 swe_time_varying$kin_summary %>% 
-  rename_kin() %>% 
   filter(age_focal == 50) %>% 
   select(kin,count_cum_dead,mean_age_lost) %>% 
   mutate_if(is.numeric, round, 2) %>% 
@@ -202,7 +197,6 @@ swe_2015_prevalence <-
 swe_2015$kin_full %>% 
   left_join(swe_2015_prevalence) %>% 
   group_by(kin, age_focal) %>% 
-  rename_kin() %>% 
   summarise(
     prevalent = sum(living * prev),
     no_prevalent = sum(living * (1-prev))
@@ -237,8 +231,8 @@ demokin_svk1980_caswell2020 <-
     f = svk_fxs,
     D = svk_pxs,
     H = svk_Hxs, 
-    birth_female=1
-    )
+    birth_female=1,
+    parity = TRUE)
 ```
 
 Note that the function ask for risks already in a certain matrix format. As an example, consider the age-parity distribution of aunts, when Focal is 20 and 60 yo (this is equivalent to Figure 4 in Caswell [2021]). 
@@ -247,8 +241,8 @@ Note that the function ask for risks already in a certain matrix format. As an e
 demokin_svk1980_caswell2020 %>% 
   filter(kin %in% c("oa","ya"), age_focal %in% c(20,60)) %>% 
   mutate(parity = as.integer(stage_kin)-1,
-         parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)),
-         parity = forcats::fct_rev(parity)) %>% 
+         parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity))
+         ) %>% 
   group_by(age_focal, age_kin, parity) %>% 
   summarise(count= sum(living)) %>% 
   ggplot() +
@@ -265,8 +259,7 @@ We can also see the portion of living daughters and mothers at different parity
 demokin_svk1980_caswell2020 %>% 
   filter(kin %in% c("d","m")) %>% 
   mutate(parity = as.integer(stage_kin)-1,
-         parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity)),
-         parity = forcats::fct_rev(parity)) %>% 
+         parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity))) %>% 
   group_by(age_focal, kin, parity) %>% 
   summarise(count= sum(living)) %>% 
   DemoKin::rename_kin() %>% 

From 89ace424c13602e231a13d544bc2934b3f6bee59 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 24 May 2023 04:30:57 -0300
Subject: [PATCH 18/89] reassign deaths

---
 NAMESPACE                   |  1 +
 R/kin.R                     | 18 +++++++++++++++++-
 R/kin2sex.R                 |  2 +-
 R/kin_multi_stage.R         | 30 +++---------------------------
 R/kin_time_invariant.R      |  2 ++
 R/kin_time_invariant_2sex.R |  3 +++
 R/kin_time_variant.R        |  3 +++
 R/kin_time_variant_2sex.R   |  3 +++
 R/plot_diagramm.R           |  2 ++
 man/dead_age_reasign.Rd     | 17 +++++++++++++++++
 10 files changed, 52 insertions(+), 29 deletions(-)
 create mode 100644 man/dead_age_reasign.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 72c7b74..7a702a6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
+export(dead_age_reasign)
 export(kin)
 export(kin2sex)
 export(kin_multi_stage)
diff --git a/R/kin.R b/R/kin.R
index b5a898b..df677e9 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -101,7 +101,7 @@ kin <- function(p = NULL, f = NULL,
   }
 
   # re-group if grouped type is asked
-  if(length(output_kin_asked)!=length(output_kin)){
+  if(!is.null(output_kin_asked) & length(output_kin_asked)!=length(output_kin)){
     if("s" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("os", "ys")]   <- "s"
     if("c" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("coa", "cya")] <- "c"
     if("a" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("oa", "ya")]   <- "a"
@@ -148,3 +148,19 @@ kin <- function(p = NULL, f = NULL,
   kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
   return(kin_out)
 }
+
+
+#' Reassign kin dead to proper Focal age
+#' @description Reassign death to proper Focal risk age
+#' @details Matrix methods set dead experience in the next age where risk happened. This function return dead experienced in x from x+1.
+#' @param kin table. A kin output in table format from function `kin_time_invariant`, `kin_time_variant`, `kin_time_invariant_2sex`, `kin_time_invariant_2sex`, `kin_multi_stage`.
+#' @export
+dead_age_reasign <- function(kin){
+  kin <- data.table::as.data.table(kin)
+  kin_dt <- kin[, -which(names(kin) == "living"), with = FALSE]
+  kin_dt$age_focal <- kin_dt$age_focal - 1
+  kin_dt <- kin_dt[kin_dt$age_focal >= 0, ]
+  kin <- merge(kin_dt, kin[, -which(names(kin) == "dead"), with = FALSE], all.y = TRUE)
+  kin$dead[is.na(kin$dead)] <- 0
+  return(kin)
+}
diff --git a/R/kin2sex.R b/R/kin2sex.R
index cd044cb..ac99530 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -107,7 +107,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead)
 
   # re-group if grouped type is asked
-  if(length(output_kin_asked)!=length(output_kin)){
+  if(!is.null(output_kin_asked) & length(output_kin_asked)!=length(output_kin)){
     if("s" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("os", "ys")]   <- "s"
     if("c" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("coa", "cya")] <- "c"
     if("a" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("oa", "ya")]   <- "a"
diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R
index bea168f..267e359 100644
--- a/R/kin_multi_stage.R
+++ b/R/kin_multi_stage.R
@@ -184,6 +184,9 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
                           }) %>%
     purrr::reduce(rbind)
 
+  # reassign dead to proper focal age
+  kin_full <- dead_age_reasign(kin_full)
+
   # results as list?
   if(list_output) {
     out <- kin_list
@@ -194,30 +197,3 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
   # end
   return(out)
 }
-
-# function to create lists for the parity case given a set of coniditonal rates and survival probabilities with stages in columns and ages in rows
-make_mulstistate_parity_matrices <- function(f_parity, p_parity, birth_female=.5){
-  ages <- nrow(f_parity)
-  stages <- ncol(f_parity) + 1
-  F_list <- U_list <- D_list <- H_list <- list()
-  for(x in 1:ages){
-    cond_probs <- as.numeric(f_parity[x,]/(1+f_parity[x,]/2))
-    U_age <- matrix(0,stages,stages)
-    diag(U_age) <-  c(1 - cond_probs, 1)
-    U_age[row(U_age)-1==col(U_age)] <- cond_probs
-    U_list[[x]] <- U_age
-    F_age <- matrix(0,stages,stages)
-    F_age[1,] <- c(cond_probs,cond_probs[stages-1])*birth_female
-    F_list[[x]] <- F_age
-  }
-  p_parity$px_last <- p_parity[,stages-1]
-  for(s in 1:stages){
-    H_age <- D_age <- matrix(0,ages,ages)
-    H_age[1,] <- 1
-    D_age[row(D_age)-1==col(D_age)] <- p_parity[-ages,s]
-    D_age[stages, stages] <- p_parity[ages,s]
-    H_list[[s]] <- H_age
-    D_list[[s]] <- D_age
-  }
-  return(list(U = U_list, F. = F_list, H = H_list, D = D_list))
-}
diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R
index 2f85412..c8e246c 100644
--- a/R/kin_time_invariant.R
+++ b/R/kin_time_invariant.R
@@ -107,6 +107,8 @@ kin_time_invariant <- function(p = NULL, f = NULL,
                ) %>%
               purrr::reduce(rbind)
 
+  # reassign dead to proper focal age
+  kin <- dead_age_reasign(kin)
 
   # results as list?
   if(list_output) {
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index e8e4c4b..fe9b0d0 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -149,6 +149,9 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   ) %>%
     purrr::reduce(rbind)
 
+  # reassign dead to proper focal age
+  kin <- dead_age_reasign(kin)
+
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index 55adffe..ff74e76 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -128,6 +128,9 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
       data.table::dcast(year + kin + age_kin + age_focal + cohort ~ alive, value.var = "count")
     }) %>% data.table::rbindlist()
 
+  # reassign dead to proper focal age
+  kin <- dead_age_reasign(kin)
+
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index cd9d560..2f34bd0 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -170,6 +170,9 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
     X <- data.table::dcast(X, year + kin + sex_kin + age_kin + age_focal + cohort ~ alive, value.var = "count", fun.aggregate = sum)
   }) %>% data.table::rbindlist()
 
+  # reassign dead to proper focal age
+  kin <- dead_age_reasign(kin)
+
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/plot_diagramm.R b/R/plot_diagramm.R
index 87d55b1..f1750f1 100644
--- a/R/plot_diagramm.R
+++ b/R/plot_diagramm.R
@@ -9,6 +9,8 @@
 plot_diagram <-
   function (kin_total, rounding = 3) {
     rels <- c("ggd", "gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys")
+    # check all types are in
+    if(!any(unique(kin_total$kin) %in% rels)) stop("You need all specific types. If some are missed or grouped, for example old and younger sisters in 's', this will fail.")
     vertices <- data.frame(
       nodes = rels
       , x = c(1, 1, 1, 1, 1, 1, 1, 0, -1, 0, -1, 2, 3, 2, 3)
diff --git a/man/dead_age_reasign.Rd b/man/dead_age_reasign.Rd
new file mode 100644
index 0000000..5599a61
--- /dev/null
+++ b/man/dead_age_reasign.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin.R
+\name{dead_age_reasign}
+\alias{dead_age_reasign}
+\title{Reassign kin dead to proper Focal age}
+\usage{
+dead_age_reasign(kin)
+}
+\arguments{
+\item{kin}{table. A kin output in table format from function \code{kin_time_invariant}, \code{kin_time_variant}, \code{kin_time_invariant_2sex}, \code{kin_time_invariant_2sex}, \code{kin_multi_stage}.}
+}
+\description{
+Reassign death to proper Focal risk age
+}
+\details{
+Matrix methods set dead experience in the next age where risk happened. This function return dead experienced in x from x+1.
+}

From 8fc1ec82bb9727da364884f4084878179755acfd Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 24 May 2023 05:26:25 -0300
Subject: [PATCH 19/89] fix reassign d

---
 NAMESPACE                      |  1 -
 R/kin.R                        | 16 ----------------
 R/kin_multi_stage.R            |  6 +++---
 R/kin_time_invariant.R         |  6 +++---
 R/kin_time_invariant_2sex.R    |  6 +++---
 R/kin_time_variant.R           |  8 ++++----
 R/kin_time_variant_2sex.R      |  8 ++++----
 man/dead_age_reasign.Rd        | 17 -----------------
 vignettes/Reference_OneSex.Rmd |  2 ++
 9 files changed, 19 insertions(+), 51 deletions(-)
 delete mode 100644 man/dead_age_reasign.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 7a702a6..72c7b74 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,7 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
-export(dead_age_reasign)
 export(kin)
 export(kin2sex)
 export(kin_multi_stage)
diff --git a/R/kin.R b/R/kin.R
index df677e9..1cd6be2 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -148,19 +148,3 @@ kin <- function(p = NULL, f = NULL,
   kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
   return(kin_out)
 }
-
-
-#' Reassign kin dead to proper Focal age
-#' @description Reassign death to proper Focal risk age
-#' @details Matrix methods set dead experience in the next age where risk happened. This function return dead experienced in x from x+1.
-#' @param kin table. A kin output in table format from function `kin_time_invariant`, `kin_time_variant`, `kin_time_invariant_2sex`, `kin_time_invariant_2sex`, `kin_multi_stage`.
-#' @export
-dead_age_reasign <- function(kin){
-  kin <- data.table::as.data.table(kin)
-  kin_dt <- kin[, -which(names(kin) == "living"), with = FALSE]
-  kin_dt$age_focal <- kin_dt$age_focal - 1
-  kin_dt <- kin_dt[kin_dt$age_focal >= 0, ]
-  kin <- merge(kin_dt, kin[, -which(names(kin) == "dead"), with = FALSE], all.y = TRUE)
-  kin$dead[is.na(kin$dead)] <- 0
-  return(kin)
-}
diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R
index 267e359..7ea156e 100644
--- a/R/kin_multi_stage.R
+++ b/R/kin_multi_stage.R
@@ -171,6 +171,9 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
   # kin_full as data.frame
   kin_full <- purrr::map2(kin_list, names(kin_list),
                           function(x,y){
+                            # reassign deaths to Focal experienced age
+                            x[(ages*s+1):(ages*s*2),1:(ages-1)] <- x[(ages*s+1):(ages*s*2),2:ages]
+                            x[(ages*s+1):(ages*s*2),ages] <- 0
                             out <- as.data.frame(x)
                             colnames(out) <- age
                             out %>%
@@ -184,9 +187,6 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
                           }) %>%
     purrr::reduce(rbind)
 
-  # reassign dead to proper focal age
-  kin_full <- dead_age_reasign(kin_full)
-
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R
index c8e246c..bbf1ba7 100644
--- a/R/kin_time_invariant.R
+++ b/R/kin_time_invariant.R
@@ -94,6 +94,9 @@ kin_time_invariant <- function(p = NULL, f = NULL,
   # reshape as data.frame
   kin <- purrr::map2(kin_list, names(kin_list),
                function(x,y){
+                    # reassign deaths to Focal experienced age
+                    x[(ages+1):(ages*2),1:(ages-1)] <- x[(ages+1):(ages*2),2:ages]
+                    x[(ages+1):(ages*2),ages] <- 0
                     out <- as.data.frame(x)
                     colnames(out) <- age
                     out %>%
@@ -107,9 +110,6 @@ kin_time_invariant <- function(p = NULL, f = NULL,
                ) %>%
               purrr::reduce(rbind)
 
-  # reassign dead to proper focal age
-  kin <- dead_age_reasign(kin)
-
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index fe9b0d0..1386ef7 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -135,6 +135,9 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   # as data.frame
   kin <- purrr::map2(kin_list, names(kin_list),
                      function(x,y){
+                       # reassign deaths to Focal experienced age
+                       x[(agess+1):(agess*2),1:(ages-1)] <- x[(agess+1):(agess*2),2:ages]
+                       x[(agess+1):(agess*2),ages] <- 0
                        out <- as.data.frame(x)
                        colnames(out) <- age
                        out %>%
@@ -149,9 +152,6 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   ) %>%
     purrr::reduce(rbind)
 
-  # reassign dead to proper focal age
-  kin <- dead_age_reasign(kin)
-
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index ff74e76..3c59c7e 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -107,10 +107,13 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
     purrr::map(~ .[selected_kin_position])
 
   # long format
-  cat("Preparing output...")
+  message("Preparing output...")
   kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
     X <- purrr::map2(X, names(X), function(x,y){
+      # reassign deaths to Focal experienced age
+      x[(ages+1):(ages*2),1:(ages-1)] <- x[(ages+1):(ages*2),2:ages]
+      x[(ages+1):(ages*2),ages] <- 0
       x <- as.data.frame(x)
       x$year <- Y
       x$kin <- y
@@ -128,9 +131,6 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
       data.table::dcast(year + kin + age_kin + age_focal + cohort ~ alive, value.var = "count")
     }) %>% data.table::rbindlist()
 
-  # reassign dead to proper focal age
-  kin <- dead_age_reasign(kin)
-
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index 2f34bd0..bcc86fc 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -148,10 +148,13 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
     purrr::keep(names(.) %in% as.character(unique(out_selected$year))) %>%
     purrr::map(~ .[selected_kin_position])
   # long format
-  cat("Preparing output...")
+  message("Preparing output...")
   kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
     X <- purrr::map2(X, names(X), function(x,y){
+      # reassign deaths to Focal experienced age
+      x[(agess+1):(agess*2),1:(ages-1)] <- x[(agess+1):(agess*2),2:ages]
+      x[(agess+1):(agess*2),ages] <- 0
       x <- data.table::as.data.table(x)
       x$year <- Y
       x$kin <- y
@@ -170,9 +173,6 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
     X <- data.table::dcast(X, year + kin + sex_kin + age_kin + age_focal + cohort ~ alive, value.var = "count", fun.aggregate = sum)
   }) %>% data.table::rbindlist()
 
-  # reassign dead to proper focal age
-  kin <- dead_age_reasign(kin)
-
   # results as list?
   if(list_output) {
     out <- kin_list
diff --git a/man/dead_age_reasign.Rd b/man/dead_age_reasign.Rd
deleted file mode 100644
index 5599a61..0000000
--- a/man/dead_age_reasign.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin.R
-\name{dead_age_reasign}
-\alias{dead_age_reasign}
-\title{Reassign kin dead to proper Focal age}
-\usage{
-dead_age_reasign(kin)
-}
-\arguments{
-\item{kin}{table. A kin output in table format from function \code{kin_time_invariant}, \code{kin_time_variant}, \code{kin_time_invariant_2sex}, \code{kin_time_invariant_2sex}, \code{kin_multi_stage}.}
-}
-\description{
-Reassign death to proper Focal risk age
-}
-\details{
-Matrix methods set dead experience in the next age where risk happened. This function return dead experienced in x from x+1.
-}
diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
index eb93a02..d2ff4b7 100644
--- a/vignettes/Reference_OneSex.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -270,6 +270,8 @@ demokin_svk1980_caswell2020 %>%
   facet_wrap(~kin, nrow = 2)
 ```
 
+This function `kin_multi_stage` can be generalized to any kind of state (be sure to set parameter `parity = FALSE`, de default).
+
 ## References
 
 Caswell, H. (2019). The formal demography of kinhip: A matrix formulation. Demographic Research 41:679–712. doi:10.4054/DemRes.2019.41.24.

From 222e0214be558f39cff8b1c43da8d3040c0427d3 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 24 May 2023 09:02:54 -0300
Subject: [PATCH 20/89] preparing for cran

---
 .Rbuildignore                            |   1 +
 DESCRIPTION                              |   2 +-
 NEWS.md                                  |   4 ++++
 R/kin.R                                  |   4 +---
 R/kin2sex.R                              |   4 +---
 README.Rmd                               |  16 +++++++++++-----
 README.md                                |  16 ++++++++++------
 cran-comments.md                         |   5 +++++
 man/figures/README-unnamed-chunk-5-1.png | Bin 199981 -> 538108 bytes
 man/kin.Rd                               |   4 +---
 man/kin2sex.Rd                           |   4 +---
 vignettes/Reference_OneSex.Rmd           |   2 +-
 vignettes/Reference_TwoSex.Rmd           |   4 ++--
 13 files changed, 39 insertions(+), 27 deletions(-)
 create mode 100644 cran-comments.md

diff --git a/.Rbuildignore b/.Rbuildignore
index cf44746..2af4302 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -2,3 +2,4 @@
 ^\.Rproj\.user$
 ^README\.Rmd$
 ^LICENSE\.md$
+^cran-comments\.md$
diff --git a/DESCRIPTION b/DESCRIPTION
index fbb511d..f8a3d43 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: DemoKin
-Title: Estimate population kin counts
+Title: Estimate Population Kin Distribution  
 Description: Estimate population kin counts and its distribution by type, age and sex.
     The package implements one-sex and two-sex framework for studying living-death availability,
     with time varying rates or not, and multi-stage model.
diff --git a/NEWS.md b/NEWS.md
index 1db9e6f..e8232f8 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,3 +4,7 @@
 * Change stable/non-stable references to time varying/non-varying rates.
 * Add multi-state process.
 
+# DemoKin 1.0.1
+* Submitted to CRAN
+* Death counts are placed in the age where Focal experience the death.
+* Aggregated kin types are allowed (`s` for older and younger sisters, for example).
diff --git a/R/kin.R b/R/kin.R
index 1cd6be2..2a3443f 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -34,14 +34,12 @@
 #' }
 #' @export
 #' @examples
-#' \dontrun{
 #' # Kin expected matrilineal count for a Swedish female based on 2015 rates.
 #' swe_surv_2015 <- swe_px[,"2015"]
 #' swe_asfr_2015 <- swe_asfr[,"2015"]
 #' # Run kinship models
 #' swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015)
-#' head(swe_2015)
-#'}
+#' head(swe_2015$kin_summary)
 
 kin <- function(p = NULL, f = NULL,
                 time_invariant = TRUE,
diff --git a/R/kin2sex.R b/R/kin2sex.R
index ac99530..3633087 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -36,15 +36,13 @@
 #' }
 #' @export
 #' @examples
-#' \dontrun{
 #' # Kin expected count by relative sex for a French female based on 2012 rates.
 #' fra_fert_f <- fra_asfr_sex[,"ff"]
 #' fra_fert_m <- fra_asfr_sex[,"fm"]
 #' fra_surv_f <- fra_surv_sex[,"pf"]
 #' fra_surv_m <- fra_surv_sex[,"pm"]
 #' fra_2012 <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m)
-#' head(fra_2012)
-#'}
+#' head(fra_2012$kin_summary)
 #'
 # get kin ----------------------------------------------------------------
 kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
diff --git a/README.Rmd b/README.Rmd
index 721e1ed..21e3d46 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -33,7 +33,13 @@ library(knitr)
 
 ## Installation
 
-You can install the development version from GitHub with:
+You can install the CRAN version:
+
+``` {r, eval=FALSE}
+install.packages("DemoKin")
+```
+
+Or the development version from GitHub with:
 
 ``` {r, eval=FALSE}
 # install.packages("devtools")
@@ -97,12 +103,12 @@ We look forward to hearing from you!
 
 ## References
 
-Caswell, H. 2019. The formal demography of kinship: A matrix formulation. Demographic Research 41:679–712. doi:10.4054/DemRes.2019.41.24.
+Caswell, H. 2019. The formal demography of kinship: A matrix formulation. Demographic Research 41:679–712.
 
-Caswell, H. 2020. The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research 42: 1097-1144. doi:10.4054/DemRes.2020.42.38.
+Caswell, H. 2020. The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research 42: 1097-1144.
 
-Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III. Kinship Dynamics with Time-Varying Demographic Rates.” Demographic Research 45: 517–46. doi:10.4054/DemRes.2021.45.16.
+Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III. Kinship Dynamics with Time-Varying Demographic Rates.” Demographic Research 45: 517–46.
 
 Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359–396. 
 
-Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27. doi:10.1016/0040-5809(74)90049-5.
+Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27.
diff --git a/README.md b/README.md
index e5d467d..daa6f18 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,13 @@ theoretical development by Goodman, Keyfitz and Pullum (1974).
 
 ## Installation
 
-You can install the development version from GitHub with:
+You can install the CRAN version:
+
+``` r
+install.packages("DemoKin")
+```
+
+Or the development version from GitHub with:
 
 ``` r
 # install.packages("devtools")
@@ -67,7 +73,7 @@ names(kin_total) <- c("kin", "count")
 plot_diagram(kin_total, rounding = 2)
 ```
 
-<img src="man/figures/README-unnamed-chunk-4-1.png" width="100%" />
+<img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
 
 Relatives are identified by a unique code:
 
@@ -127,19 +133,17 @@ request. We look forward to hearing from you!
 
 Caswell, H. 2019. The formal demography of kinship: A matrix
 formulation. Demographic Research 41:679–712.
-<doi:10.4054/DemRes.2019.41.24>.
 
 Caswell, H. 2020. The formal demography of kinship II: Multistate
 models, parity, and sibship. Demographic Research 42: 1097-1144.
-<doi:10.4054/DemRes.2020.42.38>.
 
 Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III.
 Kinship Dynamics with Time-Varying Demographic Rates.” Demographic
-Research 45: 517–46. <doi:10.4054/DemRes.2021.45.16>.
+Research 45: 517–46.
 
 Caswell, H. (2022). The formal demography of kinship IV: Two-sex models
 and their approximations. Demographic Research, 47, 359–396.
 
 Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation
 and the frequency of various kinship relationships. Theoretical
-Population Biology 5(1):1–27. <doi:10.1016/0040-5809(74)90049-5>.
+Population Biology 5(1):1–27.
diff --git a/cran-comments.md b/cran-comments.md
new file mode 100644
index 0000000..858617d
--- /dev/null
+++ b/cran-comments.md
@@ -0,0 +1,5 @@
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* This is a new release.
diff --git a/man/figures/README-unnamed-chunk-5-1.png b/man/figures/README-unnamed-chunk-5-1.png
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diff --git a/man/kin.Rd b/man/kin.Rd
index 62ec76b..2f52db6 100644
--- a/man/kin.Rd
+++ b/man/kin.Rd
@@ -69,12 +69,10 @@ kin count distribution by kin and age.
 See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by default).
 }
 \examples{
-\dontrun{
 # Kin expected matrilineal count for a Swedish female based on 2015 rates.
 swe_surv_2015 <- swe_px[,"2015"]
 swe_asfr_2015 <- swe_asfr[,"2015"]
 # Run kinship models
 swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015)
-head(swe_2015)
-}
+head(swe_2015$kin_summary)
 }
diff --git a/man/kin2sex.Rd b/man/kin2sex.Rd
index cde73cd..4a31d5d 100644
--- a/man/kin2sex.Rd
+++ b/man/kin2sex.Rd
@@ -77,14 +77,12 @@ refers to either mothers or fathers, and column \code{sex_kin} determine the sex
 See Caswell (2022) for details on formulas.
 }
 \examples{
-\dontrun{
 # Kin expected count by relative sex for a French female based on 2012 rates.
 fra_fert_f <- fra_asfr_sex[,"ff"]
 fra_fert_m <- fra_asfr_sex[,"fm"]
 fra_surv_f <- fra_surv_sex[,"pf"]
 fra_surv_m <- fra_surv_sex[,"pm"]
 fra_2012 <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m)
-head(fra_2012)
-}
+head(fra_2012$kin_summary)
 
 }
diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
index d2ff4b7..bdef30c 100644
--- a/vignettes/Reference_OneSex.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -10,7 +10,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include=FALSE}
+```{r, eval = F, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 library(devtools); load_all()
 ```
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index cc8e2c7..609b06b 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -10,12 +10,12 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include=FALSE}
+```{r, eval = F, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 library(devtools); load_all()
 ```
 
-Human males generally live longer and reproduce later than females. 
+Human males generally live shorter and reproduce later than females. 
 These sex-specific processes affect kinship dynamics in a number of ways. 
 For example, the degree to which an average member of the population, call her Focal, has a living grandparent is affected by differential mortality affecting the parental generation at older ages. 
 We may also be interested in considering how kinship structures vary by Focal's sex: a male Focal may have a different number of grandchildren than a female Focal given differences in fertility by sex. 

From 7f288e3c8387301322b07d603468abc0a251751d Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 24 May 2023 09:05:05 -0300
Subject: [PATCH 21/89] version

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index f8a3d43..e3c5609 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -3,7 +3,7 @@ Title: Estimate Population Kin Distribution
 Description: Estimate population kin counts and its distribution by type, age and sex.
     The package implements one-sex and two-sex framework for studying living-death availability,
     with time varying rates or not, and multi-stage model.
-Version: 1.0.0
+Version: 1.0.1
 Authors@R: c(
     person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"),
     person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index e8232f8..a8c2ee4 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+# DemoKin 1.0.1
+
 # DemoKin 1.0.0
 
 * Added a `NEWS.md` file to track changes to the package.

From ec457d74b47529e4de91917ebb8129a6e904c199 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 24 May 2023 09:05:20 -0300
Subject: [PATCH 22/89] Increment version number to 1.0.2

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index e3c5609..b48b899 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -3,7 +3,7 @@ Title: Estimate Population Kin Distribution
 Description: Estimate population kin counts and its distribution by type, age and sex.
     The package implements one-sex and two-sex framework for studying living-death availability,
     with time varying rates or not, and multi-stage model.
-Version: 1.0.1
+Version: 1.0.2
 Authors@R: c(
     person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"),
     person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index a8c2ee4..3896284 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+# DemoKin 1.0.2
+
 # DemoKin 1.0.1
 
 # DemoKin 1.0.0

From 377ff9489e132c00b2c4b647ddeccb6c5c3e4497 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 26 May 2023 09:16:45 -0300
Subject: [PATCH 23/89] fixing cran feedback

---
 .Rbuildignore                  |   1 +
 CRAN-SUBMISSION                |   3 +
 R/aux_funs.R                   |   2 +
 R/kin_multi_stage.R            |   4 +-
 R/kin_time_invariant.R         |   3 +
 R/kin_time_invariant_2sex.R    |   2 +-
 R/kin_time_variant.R           |   9 +-
 R/kin_time_variant_2sex.R      |   7 +-
 README.Rmd                     |   5 +-
 README.md                      |   8 +-
 dev/.DS_Store                  | Bin 6148 -> 0 bytes
 dev/TwoSex_mine.Rmd            | 203 ---------------------------------
 dev/demokin_codes.R            |  28 -----
 dev/get_HMDHFD.R               | 125 --------------------
 vignettes/Reference_OneSex.Rmd |  13 +--
 vignettes/Reference_TwoSex.Rmd |   1 +
 16 files changed, 30 insertions(+), 384 deletions(-)
 create mode 100644 CRAN-SUBMISSION
 delete mode 100644 dev/.DS_Store
 delete mode 100644 dev/TwoSex_mine.Rmd
 delete mode 100644 dev/demokin_codes.R
 delete mode 100644 dev/get_HMDHFD.R

diff --git a/.Rbuildignore b/.Rbuildignore
index 2af4302..7b0fa46 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -3,3 +3,4 @@
 ^README\.Rmd$
 ^LICENSE\.md$
 ^cran-comments\.md$
+^CRAN-SUBMISSION$
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
new file mode 100644
index 0000000..2ac4999
--- /dev/null
+++ b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 1.0.2
+Date: 2023-05-24 13:15:15 UTC
+SHA: ec457d74b47529e4de91917ebb8129a6e904c199
diff --git a/R/aux_funs.R b/R/aux_funs.R
index bead1b1..a8e7bfd 100644
--- a/R/aux_funs.R
+++ b/R/aux_funs.R
@@ -4,8 +4,10 @@
 #' @details See table `demokin_codes` to know label options.
 #' @param df data.frame. A data frame with variable `kin` with `DemoKin` codes to be labelled.
 #' @param sex character. "f" for female, "m" for male or "2sex" for both sex naming.
+#' @return Add a column with kin labels in the input data frame.
 #' @export
 rename_kin <- function(df, sex = "f"){
+  if(!"kin" %in% names(df)) stop("Input df needs a column named kin.")
   if(sex == "f") demokin_codes_sex <- DemoKin::demokin_codes[,c("DemoKin", "Labels_female")]
   if(sex == "m") demokin_codes_sex <- DemoKin::demokin_codes[,c("DemoKin", "Labels_male")]
   if(sex == "2sex") demokin_codes_sex <- DemoKin::demokin_codes[,c("DemoKin", "Labels_2sex")]
diff --git a/R/kin_multi_stage.R b/R/kin_multi_stage.R
index 7ea156e..829b232 100644
--- a/R/kin_multi_stage.R
+++ b/R/kin_multi_stage.R
@@ -14,7 +14,6 @@
 #' @return A data frame with focal´s age, related ages and type of kin
 #' (for example `d` is daughter, `oa` is older aunts, etc.), living and death kin counts, and specific stage. If `list_output = TRUE` then this is a list with elements as kin types.
 #' @export
-#'
 
 kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
                             birth_female = 1/2.04,
@@ -22,6 +21,9 @@ kin_multi_stage <- function(U = NULL, f = NULL, D = NULL, H = NULL,
                             parity = FALSE,
                             list_output = FALSE){
 
+  # global vars
+  .<-age_kin<-stage_kin<-alive<-age_focal<-count<-NULL
+
   # mandatory U as a list
   if(!is.list(U)) stop("U must be a list with age length of elements, and stage transitiotn matrix for each one.")
 
diff --git a/R/kin_time_invariant.R b/R/kin_time_invariant.R
index bbf1ba7..06058b6 100644
--- a/R/kin_time_invariant.R
+++ b/R/kin_time_invariant.R
@@ -19,6 +19,9 @@ kin_time_invariant <- function(p = NULL, f = NULL,
                         output_kin = NULL,
                         list_output = FALSE){
 
+  # global vars
+  .<-alive<-age_kin<-alive<-age_focal<-count<-NULL
+
   # make matrix transition from vectors
   age = 0:(length(p)-1)
   ages = length(age)
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index 1386ef7..07628e1 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -28,7 +28,7 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
                                     list_output = FALSE){
 
   # global vars
-  living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+  .<-sex_kin<-alive<-count<-living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
 
   # same input length
   if(!all(length(pf)==length(pm), length(pf)==length(ff), length(pf)==length(fm))) stop("Lengths of p's and f's should be the same")
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index 3c59c7e..71a5dfa 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -12,7 +12,7 @@
 #' @param birth_female numeric. Female portion at birth.
 #' @param list_output logical. Results as a list with years elements (as a result of `output_cohort` and `output_period` combination), with a second list of `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE`
 
-#' @return A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives
+#' @return A data frame of population kinship structure, with Focal's cohort, focal´s age, period year, type of relatives
 #' (for example `d` is daughter, `oa` is older aunts, etc.), living and death kin counts, and age of (living or time deceased) relatives. If `list_output = TRUE` then this is a list.
 #' @export
 
@@ -21,7 +21,7 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
                             birth_female = 1/2.04, list_output = FALSE){
 
   # global vars
-  living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+  .<-living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
 
   # check input
   if(is.null(p) | is.null(f)) stop("You need values on p and f.")
@@ -31,7 +31,7 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
 
   # data should be from same interval years
   years_data <- as.integer(colnames(p))
-  if(var(diff(years_data))!=0) stop("The years given as column names in the p and f matrices must be equally spaced.")
+  if(stats::var(diff(years_data))!=0) stop("The years given as column names in the p and f matrices must be equally spaced.")
 
   # utils
   age          <- 0:(nrow(p)-1)
@@ -151,7 +151,8 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
 #' @param pit numeric. A matrix with distribution of childbearing.
 #' @param ages numeric.
 #' @param pkin numeric. A list with kin count distribution in previous year.
-#
+#' @return A list of 14 types of kin matrices (kin age by Focal age) projected one time interval.
+#' @export
 timevarying_kin<- function(Ut, ft, pit, ages, pkin){
 
   # frequently used zero vector for initial condition
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index bcc86fc..83b3956 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -31,14 +31,14 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
                                    list_output = FALSE){
 
   # global vars
-  living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+  .<-living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
 
   # same input length
   if(!all(dim(pf) == dim(pm), dim(pf) == dim(ff), dim(pf) == dim(fm))) stop("Dimension of P's and F's should be the same")
 
   # data should be from same interval years
   years_data <- as.integer(colnames(pf))
-  if(var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again")
+  if(stats::var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again")
 
   # utils
   age          <- 0:(nrow(pf)-1)
@@ -193,7 +193,8 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
 #' @param sex_focal character. "f" for female or "m" for male.
 #' @param ages numeric.
 #' @param pkin numeric. A list with kin count distribution in previous year.
-#
+#' @return A list of 14 types of kin matrices (kin age by Focal age, blocked for two sex) projected one time interval.
+#' @export
 timevarying_kin_2sex<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
 
   agess <- ages*2
diff --git a/README.Rmd b/README.Rmd
index 21e3d46..bb1975a 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -33,13 +33,12 @@ library(knitr)
 
 ## Installation
 
+``` {r, eval=FALSE, include = F}
 You can install the CRAN version:
-
-``` {r, eval=FALSE}
 install.packages("DemoKin")
 ```
 
-Or the development version from GitHub with:
+You can install the development version from GitHub with:
 
 ``` {r, eval=FALSE}
 # install.packages("devtools")
diff --git a/README.md b/README.md
index daa6f18..ea4ece2 100644
--- a/README.md
+++ b/README.md
@@ -23,13 +23,7 @@ theoretical development by Goodman, Keyfitz and Pullum (1974).
 
 ## Installation
 
-You can install the CRAN version:
-
-``` r
-install.packages("DemoKin")
-```
-
-Or the development version from GitHub with:
+You can install the development version from GitHub with:
 
 ``` r
 # install.packages("devtools")
diff --git a/dev/.DS_Store b/dev/.DS_Store
deleted file mode 100644
index 1e69428fb8102fb057526431a60320d25eb531ae..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/dev/TwoSex_mine.Rmd b/dev/TwoSex_mine.Rmd
deleted file mode 100644
index 01ed3c0..0000000
--- a/dev/TwoSex_mine.Rmd
+++ /dev/null
@@ -1,203 +0,0 @@
----
-title: "Two-Sex expected kin counts by type of relative: A matrix implementation"
-output: 
-  html_document:
-    toc: true
-    toc_depth: 1
-vignette: >
-  %\VignetteIndexEntry{TwoSex}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include=FALSE}
-knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
-```
-
-Age distribution of Focal´s father when she born depends on male fertility pattern. Living siblings depends on sex composition (brothers and sisters) due to differential mortality risk. Intensity in care tasks is not the same between sex in many societies, so the sex of ego and his/her "sandwichness" change, because an average family network expects different roles in supporting. For these reasons, and many others, sex specific kin count estimates are important. Here we implement relations in Caswell (2022), not focusing in applications that can be analogous to the one-sex model, but in the specific advantages.
-
-```{r, message=FALSE, warning=FALSE}
-# library(DemoKin)
-library(devtools)
-load_all()
-library(tidyr)
-library(dplyr)
-library(ggplot2)
-library(knitr)
-```
-
-### 1.1. Rates by sex
-
-Female fertility by age is not a widespread available data source. Caswell (2022) takes Schoumaker (2019) makes available estimates for 160 countries, reporting  that male TFR almost always exceeds female TFR. We take the case of France in 2012 for showing how functions works (fertility and mortality data are available with the package, with column-sex values). Let´s see main differences in age distribution (TFR of 1.98 and 1.99 for males and females, practically the same)
-
-```{r}
-fra_fert_f <- fra_asfr_sex[,"ff"]
-fra_fert_m <- fra_asfr_sex[,"fm"]
-fra_surv_f <- fra_surv_sex[,"pf"]
-fra_surv_m <- fra_surv_sex[,"pm"]
-sum(fra_fert_m)-sum(fra_fert_f)
-data.frame(value = c(fra_fert_f, fra_fert_m, fra_surv_f, fra_surv_m),
-           age = rep(0:100, 4),
-           sex = rep(c(rep("f", 101), rep("m", 101)), 2),
-           risk = c(rep("fertility rate", 101 * 2), rep("survival probability", 101 * 2))) %>%
-  ggplot(aes(age, value, col=sex)) + geom_line() + facet_wrap(~ risk, scales = "free_y") + theme_bw()
-```
-
-### 1.1. Visualizing the distribution of kin
-
-Compared with one sex functions, here the user needs to specify risk by sex and decide results for which ego´s sex wants. (**this should be wrapped on a kin general formula? (note for Diego)**)
-
-```{r}
-kin_out <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
-```
-
-Let´s group aunts and siblings and see living kin by age (**should reply fig 6 (note for Diego)**).
-
-```{r}
-kin_out <- kin_out$kin_summary %>% 
-  mutate(kin = case_when(kin %in% c("s", "s") ~ "s",
-                         kin %in% c("ya", "oa") ~ "a",
-                         T ~ kin)) %>%
-  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a"))
-kin_out %>% 
-  group_by(kin, age_focal, sex_kin) %>%
-  summarise(count=sum(count_living)) %>%
-  ggplot(aes(age_focal, count, fill=sex_kin))+
-  geom_area()+
-  theme_bw() +
-  facet_wrap(~kin)
-```
-
-Kin availability by sex allows to inspect its distribution, a traditional measure in demography is the sex ratio (with females in denominator). A French woman would expect to have half grandfathers for each grandmother at 25 years old.  
-
-```{r}
-kin_out %>% 
-  group_by(kin, age_focal) %>%
-  summarise(sex_ratio=sum(count_living[sex_kin=="m"], na.rm=T)/sum(count_living[sex_kin=="f"], na.rm=T)) %>%
-  ggplot(aes(age_focal, sex_ratio))+
-  geom_line()+
-  theme_bw() +
-  facet_wrap(~kin, scales = "free")
-```
-
-How ego experiences relative deaths depends mainly on how wide is the sex-gap in mortality. She starts to lose fathers earlier than mothers. The difference on the level by sex in grandparents is due to initial availability by sex.
-
-```{r}
-# sex ratio
-kin_out %>%
-  group_by(kin, sex_kin, age_focal) %>%
-  summarise(count=sum(count_dead)) %>%
-  ggplot(aes(age_focal, count, col=sex_kin))+
-  geom_line()+
-  theme_bw() +
-  facet_wrap(~kin)
-```
-
-### 2 Approximations
-
-Caswell (2022) mentions some ways to approximate to 2-sex distribution of living kins. Here we compare the full 2-sex model that introduced before with *androgynous* variant (male fertility and survival are the same as females) and the use of GKP factors. The first comparison can be done by age, having very similar results in this case, except for grandfathers and great-grandfathers who transits higher ages and sex-gap is higher.
-
-```{r}
-kin_out <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
-kin_out_androgynous <- kin2sex(fra_surv_f, fra_surv_f, fra_fert_f, fra_fert_f, sex_focal = "f", birth_female = .5)
-bind_rows(
-  kin_out$kin_summary %>% mutate(type = "full"),
-  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous")) %>%
-  group_by(kin, age_focal, sex_kin, type) %>%
-  summarise(count = sum(count_living)) %>%
-  ggplot(aes(age_focal, count, linetype = type)) +
-  geom_line() +
-  theme_bw() +
-  theme(legend.position = "bottom", axis.text.x = element_blank()) +
-  facet_grid(row = vars(sex_kin), col = vars(kin), scales = "free")
-```
-
-Now we can multiply results from 1-sex model by the GKP factors by kin, to obtain a simple but very consistent approximation of totals (both sex) at different ages of Focal.  
-
-```{r}
-# with gkp
-kin_out_1sex <- kin(fra_surv_f, fra_fert_f, birth_female = .5)
-kin_out_GKP <- kin_out_1sex$kin_summary%>%
-  mutate(count_living = case_when(kin == "m" ~ count_living * 2,
-                            kin == "gm" ~ count_living * 4,
-                            kin == "ggm" ~ count_living * 8,
-                            kin == "d" ~ count_living * 2,
-                            kin == "gd" ~ count_living * 4,
-                            kin == "ggd" ~ count_living * 4,
-                            kin == "oa" ~ count_living * 4,
-                            kin == "ya" ~ count_living * 4,
-                            kin == "os" ~ count_living * 2,
-                            kin == "ys" ~ count_living * 2,
-                            kin == "coa" ~ count_living * 8,
-                            kin == "cya" ~ count_living * 8,
-                            kin == "nos" ~ count_living * 4,
-                            kin == "nys" ~ count_living * 4))
-
-bind_rows(
-  kin_out$kin_summary %>% mutate(type = "full"),
-  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous"),
-  kin_out_GKP %>% mutate(type = "gkp")) %>%
-  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
-                          kin %in% c("ya", "oa") ~ "a",
-                          kin %in% c("coa", "cya") ~ "c",
-                          kin %in% c("nys", "nos") ~ "n",
-                          T ~ kin)) %>%
-  filter(age_focal %in% c(5, 15, 30, 60, 80)) %>%
-  group_by(kin, age_focal, type) %>%
-  summarise(count = sum(count_living)) %>%
-  ggplot(aes(type, count)) +
-  geom_bar(aes(fill=type), stat = "identity") +
-  theme_bw()+theme(axis.text.x = element_text(angle = 90), legend.position = "bottom")+
-  facet_grid(col = vars(kin), row = vars(age_focal), scales = "free")
-```
- 
-### 2 Time variant
-
-But Focal will see his/her tree developing with current risk, being part of the evolving demographic transition, in any of its stages. Let´s compare what would be living kin for Swedish female if she would experienced time varying rates instead of period ones from 1950. We can use data already loaded in the package.
-
-```{r}
-years <- ncol(swe_px)
-ages <- nrow(swe_px)
-swe_surv_f_matrix <- swe_px
-swe_surv_m_matrix <- swe_px ^ 1.5 # this could be replaced with downloaded data from UN
-swe_fert_f_matrix <- swe_asfr
-swe_fert_m_matrix <- rbind(matrix(0, 5, years), 
-            swe_asfr[-((ages-4):ages),]) * 1.05
-par(mfrow=c(1,2))
-plot(swe_surv_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Survival probability")
-lines(swe_surv_m_matrix[,"1900"], col=2)
-plot(swe_fert_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Fertility rate")
-lines(swe_fert_m_matrix[,"1900"], col=2)
-```
-There is a n increase of living relatives because of mortality improvements, very small for grandparents because main advantages in health conditions made a huge effect in infant mortality first. Less children would have this woman in case of varying rates, due to fertility transition in the first decades in Sweden.
-
-```{r}
-kin_out_time_invariant <- kin2sex(
-                      swe_surv_f_matrix[,"1900"], swe_surv_m_matrix[,"1900"], 
-                      swe_fert_f_matrix[,"1900"], swe_fert_m_matrix[,"1900"], 
-                      sex_focal = "f", birth_female = .5)
-kin_out_time_variant <- kin2sex(
-                      swe_surv_f_matrix, swe_surv_m_matrix,
-                      swe_fert_f_matrix, swe_fert_m_matrix,
-                      sex_focal = "f",time_invariant = FALSE,
-                      birth_female = .5,
-                      output_cohort = 1900)
-
-kin_out_time_variant$kin_summary %>%
-  filter(cohort == 1900) %>% mutate(type = "variant") %>%
-  bind_rows(kin_out_time_invariant$kin_summary %>% mutate(type = "invariant")) %>% 
-  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
-                         kin %in% c("ya", "oa") ~ "a",
-                         T ~ kin)) %>%
-  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a")) %>%
-  group_by(type, kin, age_focal, sex_kin) %>%
-  summarise(count=sum(count_living)) %>%
-  ggplot(aes(age_focal, count, linetype=type))+
-  geom_line()+ theme_bw() +
-  facet_grid(cols = vars(kin), rows=vars(sex_kin), scales = "free")
-```
-
- 
-## References
-
-Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359–396. 
diff --git a/dev/demokin_codes.R b/dev/demokin_codes.R
deleted file mode 100644
index 69a545a..0000000
--- a/dev/demokin_codes.R
+++ /dev/null
@@ -1,28 +0,0 @@
-#' demokin kin codes
-
-codes <- c("coa", "cya", 'c', "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "n", "oa", "ya", "a","os", "ys", "s")
-caswell_codes <- c("t", "v", NA, "a", "b", "c", "h", "g", "d", "p", "q", NA, "r", "s", NA,"m", "n",NA)
-labels_female <- c("Cousins from older aunts", "Cousins from younger aunts", "Cousins",
-                   "Daughters", "Grand-daughters",
-                   "Great-grand-daughters", "Great-grandmothers", "Grandmothers", "Mother",
-                   "Nieces from older sisters", "Nieces from younger sisters",  "Nieces",
-                   "Aunts older than mother", "Aunts younger than mother",  "Aunts",
-                   "Older sisters", "Younger sisters", "Sisters")
-labels_male <- c("Cousins from older uncles", "Cousins from younger uncles", "Cousins",
-                 "Brothers", "Grand-sons",
-                 "Great-grand-sons", "Great-grandfathers", "Grandfathers", "Father",
-                 "Nephews from older brothers", "Nephews from younger brothers", "Nephews",
-                 "Uncles older than fathers", "Uncles younger than father", "Uncles",
-                 "Older brothers", "Younger brothers", "Brothers")
-labels_2sex <- c("Cousins from older aunts/uncles", "Cousins from younger aunts/uncles", "Cousins",
-                 "Siblings", "Grand-childrens",
-                 "Great-grand-childrens", "Great-grandfparents", "Grandparents", "Parents",
-                 "Niblings from older siblings", "Niblings from younger siblings", "Niblings",
-                 "Aunts/Uncles older than parents", "Aunts/Uncles younger than parents", "Aunts/Uncles",
-                 "Older siblings", "Younger siblings", "Siblings")
-demokin_codes <- data.frame(DemoKin = codes, Caswell = caswell_codes,
-           Labels_female = labels_female,
-           Labels_male   = labels_male,
-           Labels_2sex   = labels_2sex,
-           row.names = NULL)
-# save(demokin_codes, file = "data/demokin_codes.rda")
diff --git a/dev/get_HMDHFD.R b/dev/get_HMDHFD.R
deleted file mode 100644
index b4ee78e..0000000
--- a/dev/get_HMDHFD.R
+++ /dev/null
@@ -1,125 +0,0 @@
-#' Get time serie matrix data from HMD/HFD
-
-#' @description Wrapper function to get data of female survival, fertlity and population
-#' of selected country on selected period.
-
-#' @param country numeric. Country code from rom HMD/HFD.
-#' @param max_year numeric. Latest year to get data.
-#' @param min_year integer. Older year to get data.
-#' @param user_HMD character. From HMD.
-#' @param user_HFD character. From HFD.
-#' @param pass_HMD character. From HMD.
-#' @param pass_HFD character. From HFD.
-#' @param OAG numeric. Open age group to standarize output.
-#' @return A list wiith female survival probability, survival function, fertility and poopulation age specific matrixes, with calendar year as colnames.
-#' @export
-
-get_HMDHFD <- function(country = "SWE",
-                         min_year = 1900,
-                         max_year = 2018,
-                         user_HMD = NULL,
-                         pass_HMD = NULL,
-                         user_HFD = NULL,
-                         pass_HFD = NULL,
-                         OAG = 100){
-
-  if(any(c(is.null(user_HMD), is.null(user_HFD), is.null(pass_HMD), is.null(pass_HFD)))){
-    stop("The function needs HMD and HMF access.")
-  }
-
-  # source HMD HFD -----------------------------------------------------------------
-  pop <- HMDHFDplus::readHMDweb(CNTRY = country, "Population", user_HMD, pass_HMD, fixup = TRUE) %>%
-          dplyr::select(Year, Age, N = Female1)%>%
-          dplyr::filter(Year >= min_year, Year <= max_year)
-  lt <- HMDHFDplus::readHMDweb(country, "fltper_1x1", user_HMD, pass_HMD, fixup = TRUE) %>%
-          dplyr::filter(Year >= min_year, Year <= max_year)
-  asfr <- HMDHFDplus::readHFDweb(country, "asfrRR", user_HFD, pass_HFD, fixup = TRUE)%>%
-          dplyr::filter(Year >= min_year, Year <= max_year)
-
-  # list of yearly Leslie matrix ---------------------------------------------------
-
-  age = 0:OAG
-  ages = length(age)
-  w = last(age)
-  last_year = max(lt$Year)
-  years = min_year:last_year
-
-  # survival probability
-  px <- lt %>%
-    dplyr::filter(Age<=OAG) %>%
-    dplyr::mutate(px = 1 - qx,
-           px = ifelse(Age==OAG, 0, px)) %>%
-    dplyr::select(Year, Age, px) %>%
-    tidyr::pivot_wider(names_from = "Year", values_from = "px") %>%
-    dplyr::select(-Age) %>%
-    as.matrix()
-  rownames(px) = 0:OAG
-
-  # survival function
-  Lx <- lt %>%
-    dplyr::filter(Age<=OAG) %>%
-    dplyr::mutate(Lx = ifelse(Age==OAG, Tx, Lx)) %>%
-    dplyr::select(Year, Age, Lx) %>%
-    tidyr::pivot_wider(names_from = "Year", values_from = "Lx") %>%
-    dplyr::select(-Age) %>%
-    as.matrix()
-
-  Sx <- rbind(Lx[c(-1,-ages),]/Lx[-c(w:ages),],
-             Lx[ages,]/(Lx[w,]+Lx[ages,]),
-             Lx[ages,]/(Lx[w,]+Lx[ages,]))
-  rownames(Sx) = 0:w
-
-  # fertility
-  fx <- asfr %>%
-    dplyr::filter(Year >= min_year) %>%
-    dplyr::select(-OpenInterval) %>%
-    rbind(
-      expand.grid(Year = years,
-                  Age = c(0:(min(asfr$Age)-1),(max(asfr$Age)+1):OAG),
-                  ASFR = 0)) %>%
-    dplyr::arrange(Year, Age) %>%
-    tidyr::spread(Year, ASFR) %>%
-    dplyr::select(-Age) %>%
-    as.matrix()
-  rownames(fx) = 0:OAG
-
-  # population
-  Nx <- pop %>%
-    dplyr::mutate(Age = ifelse(Age>OAG, OAG, Age)) %>%
-    dplyr::group_by(Year, Age) %>% summarise(N = sum(N)) %>%
-    dplyr::filter(Age<=OAG, Year >= min_year) %>%
-    dplyr::arrange(Year, Age) %>%
-    tidyr::spread(Year, N) %>%
-    dplyr::select(-Age) %>%
-    as.matrix()
-  rownames(Nx) = 0:OAG
-
-  # only return data with values
-  if(any(is.na(colSums(Sx)))){
-    warning("Asked for data out of HMDHFD range")
-    Sx <- Sx[,!is.na(colSums(Sx))]
-  }
-  if(any(is.na(colSums(fx)))){
-    warning("Asked for data out of HMDHFD range")
-    fx <- fx[,!is.na(colSums(fx))]
-  }
-  if(any(is.na(colSums(Nx)))){
-    warning("Asked for data out of HMDHFD range")
-    Nx <- Nx[,!is.na(colSums(Nx))]
-  }
-
-  return(list(px=px,
-              Sx=Sx,
-              fx=fx,
-              Nx=Nx))
-}
-
-# save data
-  # swe_px <- swe_data$px
-  # swe_Sx <- swe_data$Sx
-  # swe_asfr <-swe_data$fx
-  # swe_pop <- swe_data$Nx
-  # save(swe_px, file = "data/swe_px.rda")
-  # save(swe_Sx, file = "data/swe_Sx.rda")
-  # save(swe_asfr, file = "data/swe_asfr.rda")
-  # save(swe_pop, file = "data/swe_pop.rda")
diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
index bdef30c..8f940d8 100644
--- a/vignettes/Reference_OneSex.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -82,8 +82,6 @@ swe_2015[["kin_summary"]] %>%
 Here, each relative type is identified by a unique code. Note that `DemoKin` uses different codes than Caswell (2019); the equivalence between the two set of codes is given in the following table:
 
 ```{r, fig.height=6, fig.width=8, echo=FALSE}
-library(knitr)
-
 demokin_codes %>% 
   kable
 ```
@@ -118,10 +116,10 @@ swe_2015[["kin_summary"]] %>%
 Finally, we can visualize the estimated kin counts by type of kin using a network diagram. Following with the age 35:
 
 ```{r, fig.height=6, fig.width=8, dpi=900, message=FALSE, warning=FALSE}
-  swe_2015[["kin_summary"]] %>% 
-    filter(age_focal == 35) %>% 
-    select(kin, count = count_living) %>% 
-    plot_diagram(rounding = 2)
+swe_2015[["kin_summary"]] %>% 
+  filter(age_focal == 35) %>% 
+  select(kin, count = count_living) %>% 
+  plot_diagram(rounding = 2)
 ```
 
 
@@ -190,9 +188,6 @@ swe_2015_prevalence <-
     age_kin = unique(swe_2015$kin_full$age_kin),
     prev = .005 * exp(.05 * age_kin)
     )
-
-# plot(swe_2015_prevalence)
-
 # join to kin count estimates and plot
 swe_2015$kin_full %>% 
   left_join(swe_2015_prevalence) %>% 
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index 609b06b..0bac206 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -157,6 +157,7 @@ plot(swe_surv_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Survival probabili
 lines(swe_surv_m_matrix[,"1900"], col=2)
 plot(swe_fert_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Fertility rate")
 lines(swe_fert_m_matrix[,"1900"], col=2)
+options(mfrow = NULL)
 ```
 
 We now run the time-variant two-sex models (note the `time_invariant = FALSE` argument):

From 3660c058dd067f1bc9f050227078468a1b6c89cc Mon Sep 17 00:00:00 2001
From: alburezg <gatemonte@gmail.com>
Date: Fri, 26 May 2023 17:14:32 +0200
Subject: [PATCH 24/89] updated citations

---
 README.Rmd                     | 21 +++-----
 README.md                      | 72 ++++++++++++++++++--------
 references.bib                 | 92 ++++++++++++++++++++++++++++++++++
 vignettes/Reference_OneSex.Rmd | 28 ++++-------
 vignettes/Reference_TwoSex.Rmd |  9 ++--
 vignettes/references.bib       | 92 ++++++++++++++++++++++++++++++++++
 6 files changed, 255 insertions(+), 59 deletions(-)
 create mode 100644 references.bib
 create mode 100644 vignettes/references.bib

diff --git a/README.Rmd b/README.Rmd
index bb1975a..0b6feb8 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -1,5 +1,6 @@
 ---
 output: github_document
+bibliography: vignettes\\references.bib
 ---
 
 ```{r, include = FALSE}
@@ -21,7 +22,7 @@ library(knitr)
 ::: {.column width="60%"}
 
 
-`DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell (2019, 2020, 2022), and Caswell and Song (2021). It draws on previous theoretical development by Goodman, Keyfitz and Pullum (1974).
+`DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell [-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2022], and Caswell and Song [-@caswell_formal_2021]. It draws on previous theoretical development by Goodman, Keyfitz and Pullum [-@goodman_family_1974].
 
 :::
 ::: {.column width="40%"}
@@ -47,11 +48,11 @@ devtools::install_github("IvanWilli/DemoKin")
 
 ## Usage
 
-Consider an average Swedish woman called 'Focal'. For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life (the 'time-invariant' assumption in Caswell (2019)). 
+Consider an average Swedish woman called 'Focal.' For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life; i.e., the 'time-invariant' assumption in Caswell [-@caswell_formal_2019]. 
 
 We then ask:
 
-> How many living relatives does Focal have at each age?
+> What is the expected number of relatives of Focal over her life course?
 
 Let's explore this using the Swedish data already included with `DemoKin`.
 
@@ -62,7 +63,7 @@ swe_asfr_2015 <- swe_asfr[,"2015"]
 swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
 ```
 
-*p* is the survival probability by age from a life table and *f* are the age specific fertility raties by age (see `?kin` for details). 
+*p* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). 
 
 Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or 'Keyfitz' kinship diagram with the function `plot_diagram`:
 
@@ -83,7 +84,7 @@ kable(DemoKin::demokin_codes[,c(1,3)])
 
 ## Vignette
 
-For more details, including an extension to time varying-populations rates, deceased kin, and multi-state models in a one-sex framework, see `vignette("Reference_OneSex", package = "DemoKin")`. For the case of two-sex see `vignette("Reference_TwoSex", package = "DemoKin")`.
+For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see `vignette("Reference_OneSex", package = "DemoKin")`. For two-sex models, see `vignette("Reference_TwoSex", package = "DemoKin")`.
 If the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
 
 ## Citation
@@ -101,13 +102,3 @@ If you're interested in contributing, please get in touch, create an issue, or s
 We look forward to hearing from you!
 
 ## References
-
-Caswell, H. 2019. The formal demography of kinship: A matrix formulation. Demographic Research 41:679–712.
-
-Caswell, H. 2020. The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research 42: 1097-1144.
-
-Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III. Kinship Dynamics with Time-Varying Demographic Rates.” Demographic Research 45: 517–46.
-
-Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359–396. 
-
-Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation and the frequency of various kinship relationships. Theoretical Population Biology 5(1):1–27.
diff --git a/README.md b/README.md
index ea4ece2..9edd51d 100644
--- a/README.md
+++ b/README.md
@@ -32,14 +32,15 @@ devtools::install_github("IvanWilli/DemoKin")
 
 ## Usage
 
-Consider an average Swedish woman called ‘Focal’. For this exercise, we
+Consider an average Swedish woman called ‘Focal.’ For this exercise, we
 assume a female closed population in which everyone experiences the
 Swedish 2015 mortality and fertility rates at each age throughout their
-life (the ‘time-invariant’ assumption in Caswell (2019)).
+life; i.e., the ‘time-invariant’ assumption in Caswell (2019).
 
 We then ask:
 
-> How many living relatives does Focal have at each age?
+> What is the expected number of relatives of Focal over her life
+> course?
 
 Let’s explore this using the Swedish data already included with
 `DemoKin`.
@@ -52,7 +53,7 @@ swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
 ```
 
 *p* is the survival probability by age from a life table and *f* are the
-age specific fertility raties by age (see `?kin` for details).
+age specific fertility ratios by age (see `?kin` for details).
 
 Now, we can visualize the implied kin counts (i.e., the average number
 of living kin) of Focal at age 35 using a network or ‘Keyfitz’ kinship
@@ -94,11 +95,11 @@ Relatives are identified by a unique code:
 
 ## Vignette
 
-For more details, including an extension to time varying-populations
-rates, deceased kin, and multi-state models in a one-sex framework, see
-`vignette("Reference_OneSex", package = "DemoKin")`. For the case of
-two-sex see `vignette("Reference_TwoSex", package = "DemoKin")`. If the
-vignette does not load, you may need to install the package as
+For more details, including an extension to time-variant rates, deceased
+kin, and multi-state models in a one-sex framework, see
+`vignette("Reference_OneSex", package = "DemoKin")`. For two-sex models,
+see `vignette("Reference_TwoSex", package = "DemoKin")`. If the vignette
+does not load, you may need to install the package as
 `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
 
 ## Citation
@@ -125,19 +126,48 @@ request. We look forward to hearing from you!
 
 ## References
 
-Caswell, H. 2019. The formal demography of kinship: A matrix
-formulation. Demographic Research 41:679–712.
+<div id="refs" class="references csl-bib-body hanging-indent">
 
-Caswell, H. 2020. The formal demography of kinship II: Multistate
-models, parity, and sibship. Demographic Research 42: 1097-1144.
+<div id="ref-caswell_formal_2019" class="csl-entry">
 
-Caswell, Hal and Xi Song. 2021. “The Formal Demography of Kinship. III.
-Kinship Dynamics with Time-Varying Demographic Rates.” Demographic
-Research 45: 517–46.
+Caswell, Hal. 2019. “The Formal Demography of Kinship: A Matrix
+Formulation.” *Demographic Research* 41 (September): 679–712.
+<https://doi.org/10.4054/DemRes.2019.41.24>.
 
-Caswell, H. (2022). The formal demography of kinship IV: Two-sex models
-and their approximations. Demographic Research, 47, 359–396.
+</div>
+
+<div id="ref-caswell_formal_2020" class="csl-entry">
+
+———. 2020. “The Formal Demography of Kinship II: Multistate Models,
+Parity, and Sibship.” *Demographic Research* 42 (June): 1097–1146.
+<https://doi.org/10.4054/DemRes.2020.42.38>.
+
+</div>
+
+<div id="ref-caswell_formal_2022" class="csl-entry">
+
+———. 2022. “The Formal Demography of Kinship IV: Two-Sex Models and
+Their Approximations.” *Demographic Research* 47 (September): 359–96.
+<https://doi.org/10.4054/DemRes.2022.47.13>.
+
+</div>
 
-Goodman, L.A., Keyfitz, N., and Pullum, T.W. (1974). Family formation
-and the frequency of various kinship relationships. Theoretical
-Population Biology 5(1):1–27.
+<div id="ref-caswell_formal_2021" class="csl-entry">
+
+Caswell, Hal, and Xi Song. 2021. “The Formal Demography of Kinship III:
+Kinship Dynamics with Time-Varying Demographic Rates.” *Demographic
+Research* 45 (August): 517–46.
+<https://doi.org/10.4054/DemRes.2021.45.16>.
+
+</div>
+
+<div id="ref-goodman_family_1974" class="csl-entry">
+
+Goodman, Leo A, Nathan Keyfitz, and Thomas W. Pullum. 1974. “Family
+Formation and the Frequency of Various Kinship Relationships.”
+*Theoretical Population Biology*, 27.
+<https://doi.org/10.1016/0040-5809(74)90049-5>.
+
+</div>
+
+</div>
diff --git a/references.bib b/references.bib
new file mode 100644
index 0000000..19e08e3
--- /dev/null
+++ b/references.bib
@@ -0,0 +1,92 @@
+
+@article{caswell_formal_2019,
+	title = {The formal demography of kinship: {A} matrix formulation},
+	volume = {41},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship},
+	url = {https://www.demographic-research.org/volumes/vol41/24/},
+	doi = {10.4054/DemRes.2019.41.24},
+	language = {en},
+	urldate = {2019-09-17},
+	journal = {Demographic Research},
+	author = {Caswell, Hal},
+	month = sep,
+	year = {2019},
+	pages = {679--712},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\C84MW6VX\\Caswell - 2019 - The formal demography of kinship A matrix formula.pdf:application/pdf},
+}
+
+@article{caswell_formal_2020,
+	title = {The formal demography of kinship {II}: {Multistate} models, parity, and sibship},
+	volume = {42},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship {II}},
+	url = {https://www.demographic-research.org/volumes/vol42/38/},
+	doi = {10.4054/DemRes.2020.42.38},
+	language = {en},
+	urldate = {2021-03-05},
+	journal = {Demographic Research},
+	author = {Caswell, Hal},
+	month = jun,
+	year = {2020},
+	pages = {1097--1146},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\LEHIM987\\Caswell - 2020 - The formal demography of kinship II Multistate mo.pdf:application/pdf},
+}
+
+@article{caswell_formal_2021,
+	title = {The formal demography of kinship {III}: {Kinship} dynamics with time-varying demographic rates},
+	volume = {45},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship {III}},
+	url = {https://www.demographic-research.org/volumes/vol45/16/},
+	doi = {10.4054/DemRes.2021.45.16},
+	language = {en},
+	urldate = {2021-10-19},
+	journal = {Demographic Research},
+	author = {Caswell, Hal and Song, Xi},
+	month = aug,
+	year = {2021},
+	pages = {517--546},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\W2JPMRH8\\Caswell and Song - 2021 - The formal demography of kinship III Kinship dyna.pdf:application/pdf},
+}
+
+@article{caswell_formal_2022,
+	title = {The formal demography of kinship {IV}: {Two}-sex models and their approximations},
+	volume = {47},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship {IV}},
+	url = {https://www.demographic-research.org/volumes/vol47/13/},
+	doi = {10.4054/DemRes.2022.47.13},
+	language = {en},
+	urldate = {2022-09-27},
+	journal = {Demographic Research},
+	author = {Caswell, Hal},
+	month = sep,
+	year = {2022},
+	pages = {359--396},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\CWGLWECI\\Caswell - 2022 - The formal demography of kinship IV Two-sex model.pdf:application/pdf},
+}
+
+
+@article{goodman_family_1974,
+	title = {Family {Formation} and the {Frequency} of {Various} {Kinship} {Relationships}},
+	doi = {10.1016/0040-5809(74)90049-5},
+	language = {en},
+	journal = {Theoretical Population Biology},
+	author = {Goodman, Leo A and Keyfitz, Nathan and Pullum, Thomas W.},
+	year = {1974},
+	pages = {27},
+	file = {Goodman - Family Formation and the Frequency of Various Kins.pdf:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\8ICBKYIE\\Goodman - Family Formation and the Frequency of Various Kins.pdf:application/pdf},
+}
+
+
+@book{preston_demography:_2001,
+	address = {Malden, MA},
+	title = {Demography: measuring and modeling population processes},
+	isbn = {978-1-55786-214-3 978-1-55786-451-2},
+	shorttitle = {Demography},
+	publisher = {Blackwell Publishers},
+	author = {Preston, Samuel H. and Heuveline, Patrick and Guillot, Michel},
+	year = {2001},
+	keywords = {Demography, Population research},
+}
diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
index 8f940d8..ced906e 100644
--- a/vignettes/Reference_OneSex.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -1,5 +1,6 @@
 ---
 title: "Expected kin counts by type of relative in a one-sex framework"
+bibliography: references.bib
 output: 
   html_document:
     toc: true
@@ -20,7 +21,7 @@ Here, we'll show how `DemoKin` can be used to compute the number and age distrib
 
 ## 1. Kin counts with time-invariant rates
 
-First, we compute kin counts in a **time-invariant** framework. We assume that Focal and all of her relatives experience the 2015 mortality and fertility rates throughout their entire lives (Caswell, 2019). The `DemoKin` package includes data from Sweden as an example: age-by-year matrices of survival probabilities (*swe_px*), survival ratios (*swe_Sx*), fertility rates (*swe_asfr*), and population numbers (*swe_pop*). You can see the data contained in `DemoKin` with `data(package="DemoKin")`. This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/) (see `?DemoKin::get_HMDHFD`). 
+First, we compute kin counts in a **time-invariant** framework. We assume that Focal and all of her relatives experience the 2015 mortality and fertility rates throughout their entire lives [@caswell_formal_2019]. The `DemoKin` package includes data from Sweden as an example: age-by-year matrices of survival probabilities (*swe_px*), survival ratios (*swe_Sx*), fertility rates (*swe_asfr*), and population numbers (*swe_pop*). You can see the data contained in `DemoKin` with `data(package="DemoKin")`. This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/) (see `?DemoKin::get_HMDHFD`). 
 
 In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of survival ratios and another vector of fertility rates. In this example, we get the data for the year 2015, and run the matrix models:
 
@@ -79,7 +80,7 @@ swe_2015[["kin_summary"]] %>%
   facet_wrap(~kin)
 ```
 
-Here, each relative type is identified by a unique code. Note that `DemoKin` uses different codes than Caswell (2019); the equivalence between the two set of codes is given in the following table:
+Here, each relative type is identified by a unique code. Note that `DemoKin` uses different codes than Caswell [-@caswell_formal_2019]; the equivalence between the two set of codes is given in the following table:
 
 ```{r, fig.height=6, fig.width=8, echo=FALSE}
 demokin_codes %>% 
@@ -100,8 +101,7 @@ swe_2015[["kin_full"]] %>%
 ```
 
 The one-sex model implemented in `DemoKin` assumes that the given fertility input applies to both sexes. 
-<!-- , so in case this is only for one you need to set `birth_female = 1` in a convenient way.  -->
-Note that, if using survival rates ($S_x$) instead of probabilities ($p_x$), fertility vectors should account for female person-year exposure, using: $(\frac{f_x+f_{x+1}S_x}{2})\frac{L_0}{l_0}$ instead of only $fx$ (see Preston et.al, 2002).
+Note that, if using survival rates ($S_x$) instead of probabilities ($p_x$), fertility vectors should account for female person-year exposure, using: $(\frac{f_x+f_{x+1}S_x}{2})\frac{L_0}{l_0}$ instead of only $fx$; see Preston et.al [-@preston_demography:_2001]).
 
 The `kin` function also includes a summary output with the count of living kin, mean and standard deviation of kin age, by type of kin, for each Focal's age:
 
@@ -126,7 +126,7 @@ swe_2015[["kin_summary"]] %>%
 ## 2. Kin counts with time-variant rates
 
 The demography of Sweden is, in reality, changing every year. This means that Focal and her relatives will have experienced changing mortality and fertility rates over time.
-We account for this, by using the time-variant models introduced by Caswell and Song (2021). 
+We account for this, by using the time-variant models introduced by Caswell and Song [-@caswell_formal_2021]. 
 Let's take a look at the resulting kin counts for a Focal born in 1960, limiting the output to the relative types given in the argument `output_kin`:
 
 ```{r, fig.height=6, fig.width=8}
@@ -179,7 +179,7 @@ swe_time_varying$kin_summary %>%
 
 ## 4. Prevalences
 
-Given the distribution of kin by age, we can compute the expected portion of living kin in some stage given a set of prevalences by age (e.g., a disease, employment, etc.). This is known as the Sullivan Method in the life-table literature. A matrix formulation for same results can be found in Caswell (2019), which can also be extended to a time-variant framework. 
+Given the distribution of kin by age, we can compute the expected portion of living kin in some stage given a set of prevalences by age (e.g., a disease, employment, etc.). This is known as the Sullivan Method in the life-table literature. A matrix formulation for same results can be found in Caswell [-@caswell_formal_2019], which can also be extended to a time-variant framework. 
 
 ```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
 # let´s create some prevalence by age
@@ -208,7 +208,7 @@ swe_2015$kin_full %>%
 
 `DemoKin` allows the computation of kin structures in a multi-state framework, classifying individuals jointly by age and some other feature (e.g., stages of a disease). For this, we need mortality and fertility data for each possible stage and probabilities of changing state by age.
 
-Let's consider the example of Slovakia given by Caswell (2021), where stages are parity states. 
+Let's consider the example of Slovakia given by Caswell [-@caswell_formal_2021], where stages are parity states. 
 `DemoKin` includes the data to replicate this analysis for the year 1980:
 
 - The data.frame `svk_fxs` is the fertility rate by age (rows) for each parity stage (columns). The first stage represents $parity=0$; the second stage, $parity=1$; and so on, until finally the sixth stage represents $parity\geq5$.
@@ -216,7 +216,7 @@ Let's consider the example of Slovakia given by Caswell (2021), where stages are
 - The data.frame `svk_pxs` has the same structure and represents survival probabilities. 
 - The list `svk_Uxs` has the same number of elements and ages (in this case 110, where $omega$ is 109). For each age, it contains a column-stochastic transition matrix with dimension for the state space. The entries are transition probabilities conditional on survival. 
 
-Following Caswell (2020), we can obtain the joint age-parity kin structure:
+Following Caswell [-@caswell_formal_2020], we can obtain the joint age-parity kin structure:
 
 ```{r}
 # use birth_female=1 because fertility is for female only
@@ -230,7 +230,7 @@ demokin_svk1980_caswell2020 <-
     parity = TRUE)
 ```
 
-Note that the function ask for risks already in a certain matrix format. As an example, consider the age-parity distribution of aunts, when Focal is 20 and 60 yo (this is equivalent to Figure 4 in Caswell [2021]). 
+Note that the function ask for risks already in a certain matrix format. As an example, consider the age-parity distribution of aunts, when Focal is 20 and 60 yo (this is equivalent to Figure 4 in Caswell [-@caswell_formal_2021]). 
 
 ```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
 demokin_svk1980_caswell2020 %>% 
@@ -248,7 +248,7 @@ demokin_svk1980_caswell2020 %>%
   facet_wrap(~age_focal, nrow = 2)
 ```
 
-We can also see the portion of living daughters and mothers at different parity stages over Focal's lie-course (this is equivalent to Figure 9 and 10 in Caswell [2021]). 
+We can also see the portion of living daughters and mothers at different parity stages over Focal's life-course (this is equivalent to Figure 9 and 10 in Caswell [-@caswell_formal_2021]). 
 
 ```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
 demokin_svk1980_caswell2020 %>% 
@@ -268,11 +268,3 @@ demokin_svk1980_caswell2020 %>%
 This function `kin_multi_stage` can be generalized to any kind of state (be sure to set parameter `parity = FALSE`, de default).
 
 ## References
-
-Caswell, H. (2019). The formal demography of kinhip: A matrix formulation. Demographic Research 41:679–712. doi:10.4054/DemRes.2019.41.24.
-
-Caswell, H. (2020). The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research, 42, 1097–1146.
-
-Caswell, H., & Song, X. (2021). The formal demography of kinhip III: kinhip dynamics with time-varying demographic rates. Demographic Research, 45, 517–546.
-
-Preston, S., Heuveline, P., & Guillot, M. (2000). Demography: Measuring and Modeling Population Processes. Wiley.
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index 0bac206..22dc189 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -1,5 +1,6 @@
 ---
 title: "Two-sex kinship model"
+bibliography: references.bib
 output: 
   html_document:
     toc: true
@@ -21,7 +22,7 @@ For example, the degree to which an average member of the population, call her F
 We may also be interested in considering how kinship structures vary by Focal's sex: a male Focal may have a different number of grandchildren than a female Focal given differences in fertility by sex. 
 Documenting these differences matters since women often face greater expectations to provide support and informal care to relatives.
 As they live longer, they may find themselves at greater risk of being having no living kin.
-The function `kin2sex` implements two-sex kinship models as introduced by Caswell (2022). 
+The function `kin2sex` implements two-sex kinship models as introduced by Caswell [-@caswell_formal_2022]. 
 This vignette show how to run two-sex models and highlights some of the advantages of this model over one-sex models in populations with time-invariant and time-variant rates.
 
 ```{r, message=FALSE, warning=FALSE}
@@ -200,11 +201,11 @@ kin_out_time_variant$kin_summary %>%
 
 ### 4. Approximations
 
-Caswell (2022) introduced two approaches for approximating two-sex kinship structures for cases when male demographic rates are not available. 
+Caswell [-@caswell_formal_2022] introduced two approaches for approximating two-sex kinship structures for cases when male demographic rates are not available. 
 The first is the *androgynous* approximation, which assumes equal fertility and survival for males and females.
 The second is the use of *GKP factors* apply to each type of relative (e.g., multiplying mothers by two to obtain the number of mothers and fathers). 
 
-Here, we present a visual evaluation of the accuracy of these approximations by comparing to 'true' two-sex models using the French data included with `DemoKin` for time-invariant models (Caswell, 2022). 
+Here, we present a visual evaluation of the accuracy of these approximations by comparing to 'true' two-sex models using the French data included with `DemoKin` for time-invariant models [@caswell_formal_2022]. 
 We start by considering the androgynous approximation.
 We compare expected kin counts by age and find high levels of consistency for all kin types, except for grandfathers and great-grandfathers:
 
@@ -268,5 +269,3 @@ bind_rows(
 ```
 
 ## References
-
-Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359–396. 
diff --git a/vignettes/references.bib b/vignettes/references.bib
new file mode 100644
index 0000000..19e08e3
--- /dev/null
+++ b/vignettes/references.bib
@@ -0,0 +1,92 @@
+
+@article{caswell_formal_2019,
+	title = {The formal demography of kinship: {A} matrix formulation},
+	volume = {41},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship},
+	url = {https://www.demographic-research.org/volumes/vol41/24/},
+	doi = {10.4054/DemRes.2019.41.24},
+	language = {en},
+	urldate = {2019-09-17},
+	journal = {Demographic Research},
+	author = {Caswell, Hal},
+	month = sep,
+	year = {2019},
+	pages = {679--712},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\C84MW6VX\\Caswell - 2019 - The formal demography of kinship A matrix formula.pdf:application/pdf},
+}
+
+@article{caswell_formal_2020,
+	title = {The formal demography of kinship {II}: {Multistate} models, parity, and sibship},
+	volume = {42},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship {II}},
+	url = {https://www.demographic-research.org/volumes/vol42/38/},
+	doi = {10.4054/DemRes.2020.42.38},
+	language = {en},
+	urldate = {2021-03-05},
+	journal = {Demographic Research},
+	author = {Caswell, Hal},
+	month = jun,
+	year = {2020},
+	pages = {1097--1146},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\LEHIM987\\Caswell - 2020 - The formal demography of kinship II Multistate mo.pdf:application/pdf},
+}
+
+@article{caswell_formal_2021,
+	title = {The formal demography of kinship {III}: {Kinship} dynamics with time-varying demographic rates},
+	volume = {45},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship {III}},
+	url = {https://www.demographic-research.org/volumes/vol45/16/},
+	doi = {10.4054/DemRes.2021.45.16},
+	language = {en},
+	urldate = {2021-10-19},
+	journal = {Demographic Research},
+	author = {Caswell, Hal and Song, Xi},
+	month = aug,
+	year = {2021},
+	pages = {517--546},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\W2JPMRH8\\Caswell and Song - 2021 - The formal demography of kinship III Kinship dyna.pdf:application/pdf},
+}
+
+@article{caswell_formal_2022,
+	title = {The formal demography of kinship {IV}: {Two}-sex models and their approximations},
+	volume = {47},
+	issn = {1435-9871},
+	shorttitle = {The formal demography of kinship {IV}},
+	url = {https://www.demographic-research.org/volumes/vol47/13/},
+	doi = {10.4054/DemRes.2022.47.13},
+	language = {en},
+	urldate = {2022-09-27},
+	journal = {Demographic Research},
+	author = {Caswell, Hal},
+	month = sep,
+	year = {2022},
+	pages = {359--396},
+	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\CWGLWECI\\Caswell - 2022 - The formal demography of kinship IV Two-sex model.pdf:application/pdf},
+}
+
+
+@article{goodman_family_1974,
+	title = {Family {Formation} and the {Frequency} of {Various} {Kinship} {Relationships}},
+	doi = {10.1016/0040-5809(74)90049-5},
+	language = {en},
+	journal = {Theoretical Population Biology},
+	author = {Goodman, Leo A and Keyfitz, Nathan and Pullum, Thomas W.},
+	year = {1974},
+	pages = {27},
+	file = {Goodman - Family Formation and the Frequency of Various Kins.pdf:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\8ICBKYIE\\Goodman - Family Formation and the Frequency of Various Kins.pdf:application/pdf},
+}
+
+
+@book{preston_demography:_2001,
+	address = {Malden, MA},
+	title = {Demography: measuring and modeling population processes},
+	isbn = {978-1-55786-214-3 978-1-55786-451-2},
+	shorttitle = {Demography},
+	publisher = {Blackwell Publishers},
+	author = {Preston, Samuel H. and Heuveline, Patrick and Guillot, Michel},
+	year = {2001},
+	keywords = {Demography, Population research},
+}

From 414a3da10edf65f211a574f66f0e6375978da972 Mon Sep 17 00:00:00 2001
From: alburezg <gatemonte@gmail.com>
Date: Fri, 26 May 2023 17:15:16 +0200
Subject: [PATCH 25/89] removed extra ref file

---
 references.bib | 92 --------------------------------------------------
 1 file changed, 92 deletions(-)
 delete mode 100644 references.bib

diff --git a/references.bib b/references.bib
deleted file mode 100644
index 19e08e3..0000000
--- a/references.bib
+++ /dev/null
@@ -1,92 +0,0 @@
-
-@article{caswell_formal_2019,
-	title = {The formal demography of kinship: {A} matrix formulation},
-	volume = {41},
-	issn = {1435-9871},
-	shorttitle = {The formal demography of kinship},
-	url = {https://www.demographic-research.org/volumes/vol41/24/},
-	doi = {10.4054/DemRes.2019.41.24},
-	language = {en},
-	urldate = {2019-09-17},
-	journal = {Demographic Research},
-	author = {Caswell, Hal},
-	month = sep,
-	year = {2019},
-	pages = {679--712},
-	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\C84MW6VX\\Caswell - 2019 - The formal demography of kinship A matrix formula.pdf:application/pdf},
-}
-
-@article{caswell_formal_2020,
-	title = {The formal demography of kinship {II}: {Multistate} models, parity, and sibship},
-	volume = {42},
-	issn = {1435-9871},
-	shorttitle = {The formal demography of kinship {II}},
-	url = {https://www.demographic-research.org/volumes/vol42/38/},
-	doi = {10.4054/DemRes.2020.42.38},
-	language = {en},
-	urldate = {2021-03-05},
-	journal = {Demographic Research},
-	author = {Caswell, Hal},
-	month = jun,
-	year = {2020},
-	pages = {1097--1146},
-	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\LEHIM987\\Caswell - 2020 - The formal demography of kinship II Multistate mo.pdf:application/pdf},
-}
-
-@article{caswell_formal_2021,
-	title = {The formal demography of kinship {III}: {Kinship} dynamics with time-varying demographic rates},
-	volume = {45},
-	issn = {1435-9871},
-	shorttitle = {The formal demography of kinship {III}},
-	url = {https://www.demographic-research.org/volumes/vol45/16/},
-	doi = {10.4054/DemRes.2021.45.16},
-	language = {en},
-	urldate = {2021-10-19},
-	journal = {Demographic Research},
-	author = {Caswell, Hal and Song, Xi},
-	month = aug,
-	year = {2021},
-	pages = {517--546},
-	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\W2JPMRH8\\Caswell and Song - 2021 - The formal demography of kinship III Kinship dyna.pdf:application/pdf},
-}
-
-@article{caswell_formal_2022,
-	title = {The formal demography of kinship {IV}: {Two}-sex models and their approximations},
-	volume = {47},
-	issn = {1435-9871},
-	shorttitle = {The formal demography of kinship {IV}},
-	url = {https://www.demographic-research.org/volumes/vol47/13/},
-	doi = {10.4054/DemRes.2022.47.13},
-	language = {en},
-	urldate = {2022-09-27},
-	journal = {Demographic Research},
-	author = {Caswell, Hal},
-	month = sep,
-	year = {2022},
-	pages = {359--396},
-	file = {Full Text:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\CWGLWECI\\Caswell - 2022 - The formal demography of kinship IV Two-sex model.pdf:application/pdf},
-}
-
-
-@article{goodman_family_1974,
-	title = {Family {Formation} and the {Frequency} of {Various} {Kinship} {Relationships}},
-	doi = {10.1016/0040-5809(74)90049-5},
-	language = {en},
-	journal = {Theoretical Population Biology},
-	author = {Goodman, Leo A and Keyfitz, Nathan and Pullum, Thomas W.},
-	year = {1974},
-	pages = {27},
-	file = {Goodman - Family Formation and the Frequency of Various Kins.pdf:C\:\\Users\\alburezgutierrez\\Zotero\\storage\\8ICBKYIE\\Goodman - Family Formation and the Frequency of Various Kins.pdf:application/pdf},
-}
-
-
-@book{preston_demography:_2001,
-	address = {Malden, MA},
-	title = {Demography: measuring and modeling population processes},
-	isbn = {978-1-55786-214-3 978-1-55786-451-2},
-	shorttitle = {Demography},
-	publisher = {Blackwell Publishers},
-	author = {Preston, Samuel H. and Heuveline, Patrick and Guillot, Michel},
-	year = {2001},
-	keywords = {Demography, Population research},
-}

From 1d81eec727c9df50d3fed58ef5dec87d972555a9 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 26 May 2023 16:31:26 -0300
Subject: [PATCH 26/89] fixing cran, adding biblio

---
 NAMESPACE                   | 2 ++
 R/kin.R                     | 8 ++++----
 R/kin2sex.R                 | 8 ++++----
 man/kin.Rd                  | 6 +++---
 man/kin2sex.Rd              | 8 ++++----
 man/kin_time_variant.Rd     | 2 +-
 man/rename_kin.Rd           | 3 +++
 man/timevarying_kin.Rd      | 3 +++
 man/timevarying_kin_2sex.Rd | 3 +++
 9 files changed, 27 insertions(+), 16 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index 72c7b74..0d95065 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -11,4 +11,6 @@ export(kin_time_variant_2sex)
 export(output_period_cohort_combination)
 export(plot_diagram)
 export(rename_kin)
+export(timevarying_kin)
+export(timevarying_kin_2sex)
 importFrom(magrittr,"%>%")
diff --git a/R/kin.R b/R/kin.R
index 2a3443f..0d5a060 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -5,10 +5,10 @@
 #' @details See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by default).
 #' @param p numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class
 #' in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
-#' @param f numeric. Same as p but for fertility rates.
+#' @param f numeric. Same as `p` but for fertility rates.
 #' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
-#' @param n numeric. Same as p but for population distribution (counts or `%`). Optional.
-#' @param pi numeric. Same as U but for childbearing distribution (sum to 1). Optional.
+#' @param n numeric. Only for `time_invariant = FALSE`. Same as `p` but for population distribution (counts or `%`). Optional.
+#' @param pi numeric. Same as `U` but for childbearing distribution (sum to 1). Optional.
 #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
@@ -86,7 +86,7 @@ kin <- function(p = NULL, f = NULL,
         p <- p[,as.character(output_period)]
         f <- f[,as.character(output_period)]
       }
-      kin_full <- kin_time_invariant(p = p, f = f,
+      kin_full <- kin_time_invariant(p = p, f = f, pi = pi,
                                      output_kin = output_kin, birth_female = birth_female) %>%
                               dplyr::mutate(cohort = NA, year = NA)
   }else{
diff --git a/R/kin2sex.R b/R/kin2sex.R
index 3633087..a50a438 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -7,14 +7,14 @@
 #' @details See Caswell (2022) for details on formulas.
 #' @param pf numeric. A vector (atomic) or  matrix with female probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
 #' @param pm numeric. A vector (atomic) or  matrix with male probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
-#' @param ff numeric. Same as pf but for fertility rates.
-#' @param fm numeric. Same as pm but for fertility rates.
+#' @param ff numeric. Same as `pf` but for fertility rates.
+#' @param fm numeric. Same as `pm` but for fertility rates.
 #' @param time_invariant logical. Constant assumption for a given `year` rates. Default `TRUE`.
 #' @param sex_focal character. "f" for female or "m" for male.
 #' @param pif numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default `NULL`.
 #' @param pim numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default `NULL`.
-#' @param nf numeric. Same as pf but for population distribution (counts or `%`). Optional.
-#' @param nm numeric. Same as pm but for population distribution (counts or `%`). Optional.
+#' @param nf numeric. Only for `time_invariant = FALSE`. Same as `pf` but for population distribution (counts or `%`). Optional.
+#' @param nm numeric. Only for `time_invariant = FALSE`. Same as `pm` but for population distribution (counts or `%`). Optional.
 #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
diff --git a/man/kin.Rd b/man/kin.Rd
index 2f52db6..83dbe04 100644
--- a/man/kin.Rd
+++ b/man/kin.Rd
@@ -22,13 +22,13 @@ kin(
 \item{p}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class
 in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
 
-\item{f}{numeric. Same as p but for fertility rates.}
+\item{f}{numeric. Same as \code{p} but for fertility rates.}
 
 \item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Default \code{TRUE}.}
 
-\item{pi}{numeric. Same as U but for childbearing distribution (sum to 1). Optional.}
+\item{pi}{numeric. Same as \code{U} but for childbearing distribution (sum to 1). Optional.}
 
-\item{n}{numeric. Same as p but for population distribution (counts or \verb{\%}). Optional.}
+\item{n}{numeric. Only for \code{time_invariant = FALSE}. Same as \code{p} but for population distribution (counts or \verb{\%}). Optional.}
 
 \item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.}
 
diff --git a/man/kin2sex.Rd b/man/kin2sex.Rd
index 4a31d5d..be985aa 100644
--- a/man/kin2sex.Rd
+++ b/man/kin2sex.Rd
@@ -26,9 +26,9 @@ kin2sex(
 
 \item{pm}{numeric. A vector (atomic) or  matrix with male probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
 
-\item{ff}{numeric. Same as pf but for fertility rates.}
+\item{ff}{numeric. Same as \code{pf} but for fertility rates.}
 
-\item{fm}{numeric. Same as pm but for fertility rates.}
+\item{fm}{numeric. Same as \code{pm} but for fertility rates.}
 
 \item{time_invariant}{logical. Constant assumption for a given \code{year} rates. Default \code{TRUE}.}
 
@@ -40,9 +40,9 @@ kin2sex(
 
 \item{pim}{numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default \code{NULL}.}
 
-\item{nf}{numeric. Same as pf but for population distribution (counts or \verb{\%}). Optional.}
+\item{nf}{numeric. Only for \code{time_invariant = FALSE}. Same as \code{pf} but for population distribution (counts or \verb{\%}). Optional.}
 
-\item{nm}{numeric. Same as pm but for population distribution (counts or \verb{\%}). Optional.}
+\item{nm}{numeric. Only for \code{time_invariant = FALSE}. Same as \code{pm} but for population distribution (counts or \verb{\%}). Optional.}
 
 \item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.}
 
diff --git a/man/kin_time_variant.Rd b/man/kin_time_variant.Rd
index 0646778..787052f 100644
--- a/man/kin_time_variant.Rd
+++ b/man/kin_time_variant.Rd
@@ -36,7 +36,7 @@ kin_time_variant(
 \item{list_output}{logical. Results as a list with years elements (as a result of \code{output_cohort} and \code{output_period} combination), with a second list of \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
 }
 \value{
-A data frame of population kinship structure, with focal's cohort, focal´s age, period year, type of relatives
+A data frame of population kinship structure, with Focal's cohort, focal´s age, period year, type of relatives
 (for example \code{d} is daughter, \code{oa} is older aunts, etc.), living and death kin counts, and age of (living or time deceased) relatives. If \code{list_output = TRUE} then this is a list.
 }
 \description{
diff --git a/man/rename_kin.Rd b/man/rename_kin.Rd
index ea52394..b5d195b 100644
--- a/man/rename_kin.Rd
+++ b/man/rename_kin.Rd
@@ -11,6 +11,9 @@ rename_kin(df, sex = "f")
 
 \item{sex}{character. "f" for female, "m" for male or "2sex" for both sex naming.}
 }
+\value{
+Add a column with kin labels in the input data frame.
+}
 \description{
 Add kin labels depending the sex
 }
diff --git a/man/timevarying_kin.Rd b/man/timevarying_kin.Rd
index 1826543..ac481c9 100644
--- a/man/timevarying_kin.Rd
+++ b/man/timevarying_kin.Rd
@@ -17,6 +17,9 @@ timevarying_kin(Ut, ft, pit, ages, pkin)
 
 \item{pkin}{numeric. A list with kin count distribution in previous year.}
 }
+\value{
+A list of 14 types of kin matrices (kin age by Focal age) projected one time interval.
+}
 \description{
 one time projection kin. internal function.
 }
diff --git a/man/timevarying_kin_2sex.Rd b/man/timevarying_kin_2sex.Rd
index ba4f03e..abf1774 100644
--- a/man/timevarying_kin_2sex.Rd
+++ b/man/timevarying_kin_2sex.Rd
@@ -21,6 +21,9 @@ timevarying_kin_2sex(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
 
 \item{pkin}{numeric. A list with kin count distribution in previous year.}
 }
+\value{
+A list of 14 types of kin matrices (kin age by Focal age, blocked for two sex) projected one time interval.
+}
 \description{
 one time projection kin. internal function.
 }

From b3f5387a20bd765d8567e6cfa6a8918413b1e138 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 26 May 2023 16:32:04 -0300
Subject: [PATCH 27/89] Increment version number to 1.0.3

---
 DESCRIPTION | 2 +-
 NEWS.md     | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index b48b899..62a4398 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -3,7 +3,7 @@ Title: Estimate Population Kin Distribution
 Description: Estimate population kin counts and its distribution by type, age and sex.
     The package implements one-sex and two-sex framework for studying living-death availability,
     with time varying rates or not, and multi-stage model.
-Version: 1.0.2
+Version: 1.0.3
 Authors@R: c(
     person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"),
     person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 3896284..cfc0438 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+# DemoKin 1.0.3
+
 # DemoKin 1.0.2
 
 # DemoKin 1.0.1

From f54cd023b2e6bb2de1e74d1d0c89c13828149c44 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Sun, 4 Jun 2023 10:15:04 -0300
Subject: [PATCH 28/89] preapare cran

---
 CRAN-SUBMISSION                |  6 +++---
 cran-comments.md               |  2 +-
 vignettes/Reference_TwoSex.Rmd | 19 +++++++++++++------
 3 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 2ac4999..90dd290 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
-Version: 1.0.2
-Date: 2023-05-24 13:15:15 UTC
-SHA: ec457d74b47529e4de91917ebb8129a6e904c199
+Version: 1.0.3
+Date: 2023-05-26 19:38:45 UTC
+SHA: b3f5387a20bd765d8567e6cfa6a8918413b1e138
diff --git a/cran-comments.md b/cran-comments.md
index 858617d..998da63 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -2,4 +2,4 @@
 
 0 errors | 0 warnings | 1 note
 
-* This is a new release.
+* This is a new release. I replaced the use of par(), which created some problems, with ggplot tools. Hope solves the issue.
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index 22dc189..8ee0d94 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -152,15 +152,22 @@ swe_surv_m_matrix <- swe_px ^ 1.5 # artificial perturbation for this example
 swe_fert_f_matrix <- swe_asfr
 swe_fert_m_matrix <- rbind(matrix(0, 5, years),  
             swe_asfr[-((ages-4):ages),]) * 1.05 # artificial perturbation for this example
+```
 
-par(mfrow=c(1,2))
-plot(swe_surv_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Survival probability")
-lines(swe_surv_m_matrix[,"1900"], col=2)
-plot(swe_fert_f_matrix[,"1900"], t="l", xlab = "Age", ylab = "Fertility rate")
-lines(swe_fert_m_matrix[,"1900"], col=2)
-options(mfrow = NULL)
+This is how it looks for year 1900:
+```{r}
+bind_rows(
+  data.frame(age = 0:100, sex = "Female", component = "Fertility rate", value = swe_fert_f_matrix[,"1900"]),
+  data.frame(age = 0:100, sex = "Male", component = "Fertility rate", value = swe_fert_m_matrix[,"1900"]),
+  data.frame(age = 0:100, sex = "Female", component = "Survival probability", value = swe_surv_f_matrix[,"1900"]),
+  data.frame(age = 0:100, sex = "Male", component = "Survival probability", value = swe_surv_m_matrix[,"1900"])) %>% 
+  ggplot(aes(age, value, col = sex)) +
+  geom_line() +
+  theme_bw() +
+  facet_wrap(~component, scales = "free")
 ```
 
+
 We now run the time-variant two-sex models (note the `time_invariant = FALSE` argument):
 
 ```{r}

From db6ae988af469988ad02267f66f1be43d224f2ec Mon Sep 17 00:00:00 2001
From: alburezg <gatemonte@gmail.com>
Date: Fri, 9 Jun 2023 09:36:26 +0200
Subject: [PATCH 29/89] updated readme after cran release

---
 README.Rmd | 12 ++++++-----
 README.md  | 58 ++++++++++++++++++++++++++++++++----------------------
 2 files changed, 42 insertions(+), 28 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 0b6feb8..a3f462f 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -34,12 +34,13 @@ library(knitr)
 
 ## Installation
 
-``` {r, eval=FALSE, include = F}
-You can install the CRAN version:
+Download the stable version [from CRAN](https://cran.r-project.org/web/packages/DemoKin/):
+
+``` {r, eval=FALSE, include = T}
 install.packages("DemoKin")
 ```
 
-You can install the development version from GitHub with:
+Or you can install the development version from GitHub:
 
 ``` {r, eval=FALSE}
 # install.packages("devtools")
@@ -79,12 +80,13 @@ plot_diagram(kin_total, rounding = 2)
 Relatives are identified by a unique code:
 
 ```{r, fig.height=6, fig.width=8, echo=FALSE}
-kable(DemoKin::demokin_codes[,c(1,3)])
+# kable(DemoKin::demokin_codes[,c("DemoKin", "Labels_2sex")])
+kable(DemoKin::demokin_codes[,-c(2)])
 ```
 
 ## Vignette
 
-For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see `vignette("Reference_OneSex", package = "DemoKin")`. For two-sex models, see `vignette("Reference_TwoSex", package = "DemoKin")`.
+For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see the [Reference_OneSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html) vignette; also accessible from DemoKin: `vignette("Reference_OneSex", package = "DemoKin")`. For two-sex models, see the [Reference_TwoSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html) vignette; also accessible from DemoKin: `vignette("Reference_TwoSex", package = "DemoKin")`.
 If the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
 
 ## Citation
diff --git a/README.md b/README.md
index 9edd51d..2bb3c6b 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,14 @@ theoretical development by Goodman, Keyfitz and Pullum (1974).
 
 ## Installation
 
-You can install the development version from GitHub with:
+Download the stable version [from
+CRAN](https://cran.r-project.org/web/packages/DemoKin/):
+
+``` r
+install.packages("DemoKin")
+```
+
+Or you can install the development version from GitHub:
 
 ``` r
 # install.packages("devtools")
@@ -72,33 +79,38 @@ plot_diagram(kin_total, rounding = 2)
 
 Relatives are identified by a unique code:
 
-| DemoKin | Labels_female               |
-|:--------|:----------------------------|
-| coa     | Cousins from older aunts    |
-| cya     | Cousins from younger aunts  |
-| c       | Cousins                     |
-| d       | Daughters                   |
-| gd      | Grand-daughters             |
-| ggd     | Great-grand-daughters       |
-| ggm     | Great-grandmothers          |
-| gm      | Grandmothers                |
-| m       | Mother                      |
-| nos     | Nieces from older sisters   |
-| nys     | Nieces from younger sisters |
-| n       | Nieces                      |
-| oa      | Aunts older than mother     |
-| ya      | Aunts younger than mother   |
-| a       | Aunts                       |
-| os      | Older sisters               |
-| ys      | Younger sisters             |
-| s       | Sisters                     |
+| DemoKin | Labels_female               | Labels_male                   | Labels_2sex                       |
+|:--------|:----------------------------|:------------------------------|:----------------------------------|
+| coa     | Cousins from older aunts    | Cousins from older uncles     | Cousins from older aunts/uncles   |
+| cya     | Cousins from younger aunts  | Cousins from younger uncles   | Cousins from younger aunts/uncles |
+| c       | Cousins                     | Cousins                       | Cousins                           |
+| d       | Daughters                   | Brothers                      | Siblings                          |
+| gd      | Grand-daughters             | Grand-sons                    | Grand-childrens                   |
+| ggd     | Great-grand-daughters       | Great-grand-sons              | Great-grand-childrens             |
+| ggm     | Great-grandmothers          | Great-grandfathers            | Great-grandfparents               |
+| gm      | Grandmothers                | Grandfathers                  | Grandparents                      |
+| m       | Mother                      | Father                        | Parents                           |
+| nos     | Nieces from older sisters   | Nephews from older brothers   | Niblings from older siblings      |
+| nys     | Nieces from younger sisters | Nephews from younger brothers | Niblings from younger siblings    |
+| n       | Nieces                      | Nephews                       | Niblings                          |
+| oa      | Aunts older than mother     | Uncles older than fathers     | Aunts/Uncles older than parents   |
+| ya      | Aunts younger than mother   | Uncles younger than father    | Aunts/Uncles younger than parents |
+| a       | Aunts                       | Uncles                        | Aunts/Uncles                      |
+| os      | Older sisters               | Older brothers                | Older siblings                    |
+| ys      | Younger sisters             | Younger brothers              | Younger siblings                  |
+| s       | Sisters                     | Brothers                      | Siblings                          |
 
 ## Vignette
 
 For more details, including an extension to time-variant rates, deceased
-kin, and multi-state models in a one-sex framework, see
+kin, and multi-state models in a one-sex framework, see the
+[Reference_OneSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html)
+vignette; also accessible from DemoKin:
 `vignette("Reference_OneSex", package = "DemoKin")`. For two-sex models,
-see `vignette("Reference_TwoSex", package = "DemoKin")`. If the vignette
+see the
+[Reference_TwoSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html)
+vignette; also accessible from DemoKin:
+`vignette("Reference_TwoSex", package = "DemoKin")`. If the vignette
 does not load, you may need to install the package as
 `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
 

From 2e90b913e1643eeadefac08eebc091f853e760eb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Benjamin-Samuel=20Schl=C3=BCter?=
 <benjamin-samuel.schluter@uclouvain.be>
Date: Tue, 6 Feb 2024 15:20:22 -0500
Subject: [PATCH 30/89] start building time invariant for 2 sex by cause of
 death

---
 R/kindeath_cod_time_invariant_2sex.R | 210 +++++++++++++++++++++++++++
 1 file changed, 210 insertions(+)
 create mode 100644 R/kindeath_cod_time_invariant_2sex.R

diff --git a/R/kindeath_cod_time_invariant_2sex.R b/R/kindeath_cod_time_invariant_2sex.R
new file mode 100644
index 0000000..a72fb44
--- /dev/null
+++ b/R/kindeath_cod_time_invariant_2sex.R
@@ -0,0 +1,210 @@
+#' Estimate kin counts in a time invariant framework for two-sex model.
+
+#' @description Two-sex matrix framework for kin count estimates.This produces kin counts grouped by kin, age and sex of
+#' each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
+#' are grouped in one male count of cousins.
+#' @details See Caswell (2022) for details on formulas.
+#' @param pf numeric. A vector of survival probabilities for females with same length as ages.
+#' @param ff numeric. A vector of age-specific fertility rates for females with same length as ages.
+#' @param pm numeric. A vector of survival probabilities for males with same length as ages.
+#' @param fm numeric. A vector of age-specific fertility rates for males with same length as ages.
+#' @param sex_focal character. "f" for female or "m" for male.
+#' @param birth_female numeric. Female portion at birth.
+#' @param pif numeric. For using some specific non-stable age distribution of childbearing for mothers (same length as ages). Default `NULL`.
+#' @param pim numeric. For using some specific non-stable age distribution of childbearing for fathers (same length as ages). Default `NULL`.
+#' @param output_kin character. kin to return, considering matrilineal names. For example "m" for parents, "d" for children, etc. See the `vignette` for all kin types.
+#' @param list_output logical. Results as a list with `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE`
+#'
+#' @return A data frame with focal´s age, related ages and type of kin
+#' (for example `d` is children, `oa` is older aunts/uncles, etc.), sex, alive and death. If `list_output = TRUE` then this is a list.
+#' @export
+
+## BEN: ========================================================================
+# Function building:
+library(DemoKin)
+library(tidyr)
+library(dplyr)
+
+# Input of model
+ff <- fra_asfr_sex[,"ff"]
+fm <- fra_asfr_sex[,"fm"]
+pf <- fra_surv_sex[,"pf"]
+pm <- fra_surv_sex[,"pm"]
+
+# Create a fictitious hazard matrix with three causes of death.
+# Assume that each cause consists of 1/3 of all death in all age groups.
+Hf <- Hm <- matrix(1, nrow = 3, ncol = length(ff))
+
+## =============================================================================
+
+
+# BEN: Added hazard matrices as inputs.
+#      Assume that input of cause-specific mortality will be in terms of
+#      matrices of cause-specific hazards for the two sexes (causes * ages).
+#      Alternative: a matrix (causes * ages) containing the ratio mxi/mx.
+kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
+                                    ff = NULL, fm = NULL,
+                                    Hf = NULL, Hm = NULL,
+                                    sex_focal = "f",
+                                    birth_female = 1/2.04,
+                                    pif = NULL, pim = NULL,
+                                    output_kin = NULL,
+                                    list_output = FALSE){
+
+  # global vars
+  .<-sex_kin<-alive<-count<-living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+
+  # same input length
+
+  # BEN: Now we should also check the dimensions of the cause-specific hazard
+  #      matrices.
+  if(!all(length(pf)==length(pm), length(pf)==length(ff), length(pf)==length(fm),
+          nrow(Hf)==nrow(Hm), ncol(Hf)==ncol(Hm), ncol(Hf)==length(pf))) stop("Number of age groups of p's, h's, and f's should match")
+
+  # make matrix transition from vectors. Include death counts with matrix M
+  age = 0:(length(pf)-1)
+  ages = length(age)
+  agess = ages * 2
+  Uf = Um = Ff = Fm = Gt = zeros = matrix(0, nrow=ages, ncol=ages)
+  Uf[row(Uf)-1 == col(Uf)] <- pf[-ages]
+
+  # BEN: What is the purpose of the following line? By default it is zero due to
+  #      how the matrix is created
+  Uf[ages, ages] = Uf[ages]
+
+  Um[row(Um)-1 == col(Um)] <- pm[-ages]
+  Um[ages, ages] = Um[ages]
+
+  # BEN: Building of M, matrix of cause-specific prob. of dying.
+  #      Hence, M = H D(h_tilde)^{-1} D(q)
+  #      where h_tilde are the summed hazards for each age, and
+  #      q = 1 - p
+  alpha <- nrow(Hf) # number of causes of death
+  sum_hf <- t(rep(1, alpha)) %*% Hf # h_tilde female
+  sum_hm <- t(rep(1, alpha)) %*% Hm # h_tilde male
+  Mf <- Hf %*% solve(diag(c(sum_hf))) %*% diag(1-pf)
+  Mm <- Hm %*% solve(diag(c(sum_hm))) %*% diag(1-pm)
+  # Mm <- diag(1-pm)
+  # Mf <- diag(1-pf)
+  zeros_l <- matrix(0, nrow = ages, ncol = alpha) # zero matrix for living kin part
+  zeros_d = matrix(0, nrow = alpha, ncol = alpha) # zero matrix for death kin part
+  Ut <- as.matrix(rbind(
+    cbind(Matrix::bdiag(Uf, Um), Matrix::bdiag(zeros_l, zeros_l)),
+    cbind(Matrix::bdiag(Mf, Mm), Matrix::bdiag(zeros_d, zeros_d))))
+
+  Ff[1,] = ff
+  Fm[1,] = fm
+
+  # BEN: CONTINUE WORK FROM HERE
+  Ft <- Ft_star <- matrix(0, agess*2, agess*2)
+  Ft[1:agess,1:agess] <- rbind(cbind(birth_female * Ff, birth_female * Fm),
+                               cbind((1-birth_female) * Ff, (1-birth_female) * Fm))
+
+  # mother and father do not reproduce independently to produce focal´s siblings. Assign to mother
+  Ft_star[1:agess,1:ages] <- rbind(birth_female * Ff, (1-birth_female) * Ff)
+
+  # parents age distribution under stable assumption in case no input
+  if(is.null(pim) | is.null(pif)){
+    A = Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
+    A_decomp = eigen(A)
+    lambda = as.double(A_decomp$values[1])
+    w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+    wf = w[1:ages]
+    wm = w[(ages+1):(2*ages)]
+    pif = wf * ff / sum(wf * ff)
+    pim = wm * fm / sum(wm * fm)
+  }
+
+  # initial count matrix (kin ages in rows and focal age in column)
+  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, agess*2, ages)
+
+  # locate focal at age 0 depending sex
+  sex_index <- ifelse(sex_focal == "f", 1, ages+1)
+  phi[sex_index, 1] <- 1
+
+  # G matrix moves focal by age
+  G <- matrix(0, nrow=ages, ncol=ages)
+  G[row(G)-1 == col(G)] <- 1
+  Gt <- matrix(0, agess*2, agess*2)
+  Gt[1:(agess), 1:(agess)] <- as.matrix(Matrix::bdiag(G, G))
+
+  # focal´s trip
+  # names of matrix count by kin refers to matrilineal as general reference
+  m[1:(agess),1] = c(pif, pim)
+  for(i in 1:(ages-1)){
+    # i = 1
+    phi[,i+1] = Gt %*% phi[,i]
+    d[,i+1]   = Ut %*% d[,i]   + Ft %*% phi[,i]
+    gd[,i+1]  = Ut %*% gd[,i]  + Ft %*% d[,i]
+    ggd[,i+1] = Ut %*% ggd[,i] + Ft %*% gd[,i]
+    m[,i+1]   = Ut %*% m[,i]
+    ys[,i+1]  = Ut %*% ys[,i]  + Ft_star %*% m[,i]
+    nys[,i+1] = Ut %*% nys[,i] + Ft %*% ys[,i]
+  }
+
+  gm[1:(agess),1] = m[1:(agess),] %*% (pif + pim)
+  for(i in 1:(ages-1)){
+    gm[,i+1]  = Ut %*% gm[,i]
+  }
+
+  ggm[1:(agess),1] = gm[1:(agess),] %*% (pif + pim)
+  for(i in 1:(ages-1)){
+    ggm[,i+1]  = Ut %*% ggm[,i]
+  }
+
+  os[1:(agess),1]  = d[1:(agess),] %*% pif
+  nos[1:(agess),1] = gd[1:(agess),] %*% pif
+  for(i in 1:(ages-1)){
+    os[,i+1]  = Ut %*% os[,i]
+    nos[,i+1] = Ut %*% nos[,i] + Ft %*% os[,i]
+  }
+
+  oa[1:(agess),1]  = os[1:(agess),] %*% (pif + pim)
+  ya[1:(agess),1]  = ys[1:(agess),] %*% (pif + pim)
+  coa[1:(agess),1] = nos[1:(agess),] %*% (pif + pim)
+  cya[1:(agess),1] = nys[1:(agess),] %*% (pif + pim)
+  for(i in 1:(ages-1)){
+    oa[,i+1]  = Ut %*% oa[,i]
+    ya[,i+1]  = Ut %*% ya[,i]  + Ft_star %*% gm[,i]
+    coa[,i+1] = Ut %*% coa[,i] + Ft %*% oa[,i]
+    cya[,i+1] = Ut %*% cya[,i] + Ft %*% ya[,i]
+  }
+
+  # get results
+  kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
+                   nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
+
+  # only selected kin
+  if(!is.null(output_kin)){
+    kin_list <- kin_list %>% purrr::keep(names(.) %in% output_kin)
+  }
+
+  # as data.frame
+  kin <- purrr::map2(kin_list, names(kin_list),
+                     function(x,y){
+                       # reassign deaths to Focal experienced age
+                       x[(agess+1):(agess*2),1:(ages-1)] <- x[(agess+1):(agess*2),2:ages]
+                       x[(agess+1):(agess*2),ages] <- 0
+                       out <- as.data.frame(x)
+                       colnames(out) <- age
+                       out %>%
+                         dplyr::mutate(kin = y,
+                                       age_kin = rep(age,4),
+                                       sex_kin = rep(c(rep("f",ages), rep("m",ages)),2),
+                                       alive = c(rep("living",2*ages), rep("dead",2*ages))) %>%
+                         tidyr::pivot_longer(c(-age_kin, -kin, -sex_kin, -alive), names_to = "age_focal", values_to = "count") %>%
+                         dplyr::mutate(age_focal = as.integer(age_focal)) %>%
+                         tidyr::pivot_wider(names_from = alive, values_from = count)
+                     }
+  ) %>%
+    purrr::reduce(rbind)
+
+  # results as list?
+  if(list_output) {
+    out <- kin_list
+  }else{
+    out <- kin
+  }
+
+  return(out)
+}

From 6cdccb5c12047e1c3e0c5fa46b9b4fe4aa4ee5ad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Benjamin-Samuel=20Schl=C3=BCter?=
 <benjamin-samuel.schluter@uclouvain.be>
Date: Tue, 13 Feb 2024 15:07:07 -0500
Subject: [PATCH 31/89] created functions for cod

---
 ...t_2sex.R => kin_time_invariant_2sex_cod.R} | 141 +++++--
 R/kin_time_variant_2sex_cod.R                 | 356 ++++++++++++++++++
 2 files changed, 460 insertions(+), 37 deletions(-)
 rename R/{kindeath_cod_time_invariant_2sex.R => kin_time_invariant_2sex_cod.R} (61%)
 create mode 100644 R/kin_time_variant_2sex_cod.R

diff --git a/R/kindeath_cod_time_invariant_2sex.R b/R/kin_time_invariant_2sex_cod.R
similarity index 61%
rename from R/kindeath_cod_time_invariant_2sex.R
rename to R/kin_time_invariant_2sex_cod.R
index a72fb44..0434360 100644
--- a/R/kindeath_cod_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex_cod.R
@@ -21,19 +21,25 @@
 
 ## BEN: ========================================================================
 # Function building:
-library(DemoKin)
-library(tidyr)
-library(dplyr)
-
-# Input of model
-ff <- fra_asfr_sex[,"ff"]
-fm <- fra_asfr_sex[,"fm"]
-pf <- fra_surv_sex[,"pf"]
-pm <- fra_surv_sex[,"pm"]
-
-# Create a fictitious hazard matrix with three causes of death.
-# Assume that each cause consists of 1/3 of all death in all age groups.
-Hf <- Hm <- matrix(1, nrow = 3, ncol = length(ff))
+# library(DemoKin)
+# library(tidyr)
+# library(dplyr)
+# library(here)
+#
+# # Input of model
+# ff <- fra_asfr_sex[,"ff"]
+# fm <- fra_asfr_sex[,"fm"]
+# pf <- fra_surv_sex[,"pf"]
+# pm <- fra_surv_sex[,"pm"]
+# birth_female = 1/2.04
+# pif <- pim <- NULL
+# sex_focal = "f"
+# output_kin = NULL
+# list_output = FALSE
+#
+# # Create a fictitious hazard matrix with three causes of death.
+# # Assume that each cause consists of 1/3 of all death in all age groups.
+# Hf <- Hm <- matrix(1, nrow = 3, ncol = length(ff))
 
 ## =============================================================================
 
@@ -42,14 +48,19 @@ Hf <- Hm <- matrix(1, nrow = 3, ncol = length(ff))
 #      Assume that input of cause-specific mortality will be in terms of
 #      matrices of cause-specific hazards for the two sexes (causes * ages).
 #      Alternative: a matrix (causes * ages) containing the ratio mxi/mx.
-kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
-                                    ff = NULL, fm = NULL,
-                                    Hf = NULL, Hm = NULL,
-                                    sex_focal = "f",
-                                    birth_female = 1/2.04,
-                                    pif = NULL, pim = NULL,
-                                    output_kin = NULL,
-                                    list_output = FALSE){
+kin_time_invariant_2sex_cod <- function(pf = NULL,
+                                        pm = NULL,
+                                        ff = NULL,
+                                        fm = NULL,
+                                        Hf = NULL,
+                                        Hm = NULL,
+                                        sex_focal = "f",
+                                        birth_female = 1 / 2.04,
+                                        pif = NULL,
+                                        pim = NULL,
+                                        output_kin = NULL,
+                                        list_output = FALSE) {
+
 
   # global vars
   .<-sex_kin<-alive<-count<-living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
@@ -65,7 +76,15 @@ kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   age = 0:(length(pf)-1)
   ages = length(age)
   agess = ages * 2
-  Uf = Um = Ff = Fm = Gt = zeros = matrix(0, nrow=ages, ncol=ages)
+  Uf = Um = Ff = Fm = Gt = matrix(0, nrow=ages, ncol=ages)
+
+  # BEN: The zero matrix was deleted from line above and has
+  #      to be made specific according to living/dead kin
+  #      part of the block matrix Ut.
+  causes <- nrow(Hf) # number of causes of death
+  zeros_l <- matrix(0, nrow = ages, ncol = (causes*ages)) # zero matrix for living kin part
+  zeros_d = matrix(0, nrow = (causes*ages), ncol = (causes*ages)) # zero matrix for death kin part
+
   Uf[row(Uf)-1 == col(Uf)] <- pf[-ages]
 
   # BEN: What is the purpose of the following line? By default it is zero due to
@@ -79,24 +98,30 @@ kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   #      Hence, M = H D(h_tilde)^{-1} D(q)
   #      where h_tilde are the summed hazards for each age, and
   #      q = 1 - p
-  alpha <- nrow(Hf) # number of causes of death
-  sum_hf <- t(rep(1, alpha)) %*% Hf # h_tilde female
-  sum_hm <- t(rep(1, alpha)) %*% Hm # h_tilde male
+  sum_hf <- t(rep(1, causes)) %*% Hf # h_tilde female
+  sum_hm <- t(rep(1, causes)) %*% Hm # h_tilde male
   Mf <- Hf %*% solve(diag(c(sum_hf))) %*% diag(1-pf)
   Mm <- Hm %*% solve(diag(c(sum_hm))) %*% diag(1-pm)
   # Mm <- diag(1-pm)
   # Mf <- diag(1-pf)
-  zeros_l <- matrix(0, nrow = ages, ncol = alpha) # zero matrix for living kin part
-  zeros_d = matrix(0, nrow = alpha, ncol = alpha) # zero matrix for death kin part
+
+  # BEN: In order to classify kin death by both cause and age at death,
+  #      we need a mortality matrices M_hat of dimension
+  #      ((causes*ages) * ages). See eq.12 in Caswell et al. (2024).
+  # Store columns of M as a list of vectors
+  Mf.cols <- lapply(1:ncol(Mf), function(j) return(Mf[,j]))
+  Mm.cols <- lapply(1:ncol(Mm), function(j) return(Mm[,j]))
+  # Create M_hat using the vectors as elements of the block diagonal
   Ut <- as.matrix(rbind(
     cbind(Matrix::bdiag(Uf, Um), Matrix::bdiag(zeros_l, zeros_l)),
-    cbind(Matrix::bdiag(Mf, Mm), Matrix::bdiag(zeros_d, zeros_d))))
+    cbind(Matrix::bdiag(Matrix::bdiag(Mf.cols), Matrix::bdiag(Mm.cols)), Matrix::bdiag(zeros_d, zeros_d))))
 
   Ff[1,] = ff
   Fm[1,] = fm
 
-  # BEN: CONTINUE WORK FROM HERE
-  Ft <- Ft_star <- matrix(0, agess*2, agess*2)
+  # BEN: Accounting for causes of death leads to have different dimensions
+  #      in Ft and Ft_star.
+  Ft <- Ft_star <- matrix(0, (agess + agess*causes), (agess + agess*causes))
   Ft[1:agess,1:agess] <- rbind(cbind(birth_female * Ff, birth_female * Fm),
                                cbind((1-birth_female) * Ff, (1-birth_female) * Fm))
 
@@ -116,7 +141,8 @@ kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   }
 
   # initial count matrix (kin ages in rows and focal age in column)
-  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, agess*2, ages)
+  # BEN: Changed dimensions of lower part (dead kin) to account for death from causes.
+  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, (agess + agess*causes), ages)
 
   # locate focal at age 0 depending sex
   sex_index <- ifelse(sex_focal == "f", 1, ages+1)
@@ -125,7 +151,10 @@ kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   # G matrix moves focal by age
   G <- matrix(0, nrow=ages, ncol=ages)
   G[row(G)-1 == col(G)] <- 1
-  Gt <- matrix(0, agess*2, agess*2)
+
+  # BEN: Changed dimensions
+  Gt <- matrix(0, (agess + agess*causes), (agess + agess*causes))
+
   Gt[1:(agess), 1:(agess)] <- as.matrix(Matrix::bdiag(G, G))
 
   # focal´s trip
@@ -182,16 +211,24 @@ kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
   # as data.frame
   kin <- purrr::map2(kin_list, names(kin_list),
                      function(x,y){
+
+                       # BEN: Death take place in the same year and age!
+                       #      I adapted the code
+                       #      below such that it works with the new dimensions.
+
                        # reassign deaths to Focal experienced age
-                       x[(agess+1):(agess*2),1:(ages-1)] <- x[(agess+1):(agess*2),2:ages]
-                       x[(agess+1):(agess*2),ages] <- 0
+                       x[(agess+1):(agess + agess*causes),1:(ages-1)] <- x[(agess+1):(agess + agess*causes),2:ages]
+                       x[(agess+1):(agess + agess*causes),ages] <- 0
                        out <- as.data.frame(x)
                        colnames(out) <- age
                        out %>%
+                         # BEN: the matrices have different dimensions when
+                         #      we accounf for causes of death so what follows
+                         #      has been substantially changed.
                          dplyr::mutate(kin = y,
-                                       age_kin = rep(age,4),
-                                       sex_kin = rep(c(rep("f",ages), rep("m",ages)),2),
-                                       alive = c(rep("living",2*ages), rep("dead",2*ages))) %>%
+                                       age_kin = c(rep(age,2), rep(rep(age,each=causes),2)),
+                                       sex_kin = c(rep(c("f", "m"),each=ages), rep(c("f", "m"),each=ages*causes)),
+                                       alive = c(rep("living",2*ages), rep(paste0("deadcause",1:causes),2*ages))) %>%
                          tidyr::pivot_longer(c(-age_kin, -kin, -sex_kin, -alive), names_to = "age_focal", values_to = "count") %>%
                          dplyr::mutate(age_focal = as.integer(age_focal)) %>%
                          tidyr::pivot_wider(names_from = alive, values_from = count)
@@ -208,3 +245,33 @@ kindeath_cod_time_invariant_2sex <- function(pf = NULL, pm = NULL,
 
   return(out)
 }
+
+## BEN: ========================================================================
+
+# Checks
+
+# No dead parent at birth: deadcausei=0 when age_focal==0
+# ff # fertility starts at age 13
+# kin |> filter(kin == "m", age_focal ==0, age_kin >= 12)
+#
+# # pi when age_focal==0 and age_kin when fx>0:
+# kin |> filter(kin == "m", age_kin >= 13, age_focal ==0)
+# pif[14:101]
+#
+# # mother dying from cause i at age x when focal is age==1 comes from nber of
+# # living mother age x when focal is age==1 multiplied by (1-pf[x])*(1/3)
+# kin |> filter(kin == "m", age_kin == 14, age_focal ==1)
+# 0.000246 * ((1-pf[15])*(1/3)) # mother
+# 0.0000486 * ((1-pm[15])*(1/3)) # father
+#
+# # Store to compare with kin_time_invariant_2sex.R
+# saveRDS(
+#   kin,
+#   here(
+#     "checks",
+#     "output_time_invariant_2sex.rds"
+#   )
+# )
+
+
+## =============================================================================
diff --git a/R/kin_time_variant_2sex_cod.R b/R/kin_time_variant_2sex_cod.R
new file mode 100644
index 0000000..0d32652
--- /dev/null
+++ b/R/kin_time_variant_2sex_cod.R
@@ -0,0 +1,356 @@
+#' Estimate kin counts in a time variant framework (dynamic rates) in a two-sex framework (Caswell, 2022)
+
+#' @description Two-sex matrix framework for kin count estimates with varying rates.
+#' This produces kin counts grouped by kin, age and sex of each relatives at each Focal´s age.
+#' For example, male cousins from aunts and uncles from different sibling's parents are grouped in one male count of cousins.
+#' @details See Caswell (2022) for details on formulas.
+#' @param pf numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param pm numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
+#' @param ff numeric. Same as pf but for fertility rates.
+#' @param fm numeric. Same as pm but for fertility rates.
+#' @param sex_focal character. "f" for female or "m" for male.
+#' @param pif numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default `NULL`.
+#' @param pim numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default `NULL`.
+#' @param nf numeric. Same as pf but for population distribution (counts or `%`). Optional.
+#' @param nm numeric. Same as pm but for population distribution (counts or `%`). Optional.
+#' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
+#' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
+#' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
+#' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
+#' @param list_output logical. Results as a list with years elements (as a result of `output_cohort` and `output_period` combination), with a second list of `output_kin` elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default `FALSE`
+#' @return A data.frame with year, cohort, Focal´s age, related ages, sex and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age and sex.
+#' @export
+
+
+## BEN: ========================================================================
+# Function building:
+library(DemoKin)
+library(tidyr)
+library(dplyr)
+library(here)
+
+# Input of model
+years <- ncol(swe_px)
+ages <- nrow(swe_px)
+ff <- swe_asfr
+fm <- rbind(matrix(0, 5, years),
+            swe_asfr[-((ages-4):ages),]) * 1.05
+pf <- swe_px
+pm <- swe_px ^ 1.5
+
+sex_focal = "f"
+time_invariant = FALSE
+birth_female = .5
+output_cohort = 1900 # like in the vignette
+output_period = NULL
+output_kin = NULL
+
+
+pif <- pim <- NULL
+nf <- nm <- NULL
+list_output = FALSE
+
+# Create a fictitious hazard matrix with three causes of death, where each
+# year is a list item (Hazard matrix needs to be (causes * ages) for the
+# matrix algebra to work well with existing code).
+H <- matrix(c(0.5, 1, 2), nrow = 3, ncol = nrow(pf))
+Hf <- Hm <- sapply(colnames(pf), function(x) {
+  return(H)
+  },
+  simplify = FALSE,
+  USE.NAMES = TRUE
+  )
+# BEN: Load time invariant for COD
+source("./R/kin_time_invariant_2sex_cod.R")
+
+
+## =============================================================================
+
+
+# BEN: Added hazard matrices as inputs.
+#      Assume that input of cause-specific mortality will be in terms of
+#      matrices of cause-specific hazards for the two sexes (causes * ages).
+#      Alternative: a matrix (causes * ages) containing the ratio mxi/mx.
+kin_time_variant_2sex_cod <- function(pf = NULL, pm = NULL,
+                                  ff = NULL, fm = NULL,
+                                  Hf = NULL, Hm = NULL,
+                                  sex_focal = "f",
+                                  birth_female = 1/2.04,
+                                  pif = NULL, pim = NULL,
+                                  nf = NULL, nm = NULL,
+                                  output_cohort = NULL, output_period = NULL, output_kin = NULL,
+                                  list_output = FALSE){
+
+  # global vars
+  .<-living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
+
+  # same input length
+
+  # BEN: Now we should also check the dimensions of the cause-specific hazard
+  #      matrices.
+  if(!all(dim(pf) == dim(pm), dim(pf) == dim(ff), dim(pf) == dim(fm),
+          nrow(Hf)==nrow(Hm), ncol(Hf)==ncol(Hm), ncol(Hf)==nrow(pf),
+          length(Hf)==length(Hm), length(Hm)==ncol(pf))) stop("Dimension of P's, F's, and H's should match")
+
+  # data should be from same interval years
+  years_data <- as.integer(colnames(pf))
+  if(stats::var(diff(years_data))!=0) stop("Data should be for same interval length years. Fill the gaps and run again")
+
+  # utils
+  age          <- 0:(nrow(pf)-1)
+  n_years_data <- length(years_data)
+  ages         <- length(age)
+  agess        <- ages*2
+  om           <- max(age)
+
+  # BEN: The zero matrix was deleted from line above and has
+  #      to be made specific according to living/dead kin
+  #      part of the block matrix Ut.
+  causes <- nrow(Hf[[1]]) # number of causes of death
+  zeros_l <- matrix(0, nrow = ages, ncol = (causes*ages)) # zero matrix for living kin part
+  zeros_d = matrix(0, nrow = (causes*ages), ncol = (causes*ages)) # zero matrix for death kin part
+
+  # age distribution at child born
+  Pif <- pif; no_Pif <- FALSE
+  Pim <- pim; no_Pim <- FALSE
+  if(is.null(pif)){
+    if(!is.null(nf)){
+      Pif <- t(t(nf * ff)/colSums(nf * ff))
+    }else{
+      Pif <- matrix(0, nrow=ages, ncol=n_years_data)
+      no_Pif <- TRUE
+    }
+  }
+  if(is.null(pim)){
+    if(!is.null(nm)){
+      Pim <- t(t(nm * fm)/colSums(nm * fm))
+    }else{
+      Pim <- matrix(0, nrow=ages, ncol=n_years_data)
+      no_Pim <- TRUE
+    }
+  }
+
+  # get lists of matrix
+  Ul = Fl = Fl_star = list()
+  kin_all <- list()
+  pb <- progress::progress_bar$new(
+    format = "Running over input years [:bar] :percent",
+    total = n_years_data + 1, clear = FALSE, width = 60)
+
+  # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+  # BEN: First load function at the end of script
+  # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
+  for(t in 1:n_years_data){
+    # t = 1
+    Uf = Um = Fft = Fmt = Mm = Mf = Gt = matrix(0, nrow=ages, ncol=ages)
+    Uf[row(Uf)-1 == col(Uf)] <- pf[-ages,t]
+    Uf[ages, ages] = pf[ages,t]
+    Um[row(Um)-1 == col(Um)] <- pm[-ages,t]
+    Um[ages, ages] = pm[ages,t]
+
+    # BEN: Building of M, matrix of cause-specific prob. of dying.
+    #      Hence, M = H D(h_tilde)^{-1} D(q)
+    #      where h_tilde are the summed hazards for each age, and
+    #      q = 1 - p
+    sum_hf <- t(rep(1, causes)) %*% Hf[[t]] # h_tilde female
+    sum_hm <- t(rep(1, causes)) %*% Hm[[t]] # h_tilde male
+    Mf <- Hf[[t]] %*% solve(diag(c(sum_hf))) %*% diag(1-pf[,t])
+    Mm <- Hm[[t]] %*% solve(diag(c(sum_hm))) %*% diag(1-pm[,t])
+    # Mm <- diag(1-pm[,t])
+    # Mf <- diag(1-pf[,t])
+
+    # BEN: In order to classify kin death by both cause and age at death,
+    #      we need a mortality matrices M_hat of dimension
+    #      ((causes*ages) * ages). See eq.12 in Caswell et al. (2024).
+    # Store columns of M as a list of vectors
+    Mf.cols <- lapply(1:ncol(Mf), function(j) return(Mf[,j]))
+    Mm.cols <- lapply(1:ncol(Mm), function(j) return(Mm[,j]))
+    # Create M_hat using the vectors as elements of the block diagonal
+    Ut <- as.matrix(rbind(
+      cbind(Matrix::bdiag(Uf, Um), Matrix::bdiag(zeros_l, zeros_l)),
+      cbind(Matrix::bdiag(Matrix::bdiag(Mf.cols), Matrix::bdiag(Mm.cols)), Matrix::bdiag(zeros_d, zeros_d))))
+
+    Ul[[as.character(years_data[t])]] <- Ut
+    Fft[1,] = ff[,t]
+    Fmt[1,] = fm[,t]
+
+    # BEN: Accounting for causes of death leads to have different dimensions
+    #      in Ft and Ft_star.
+    Ft <- Ft_star <- matrix(0, (agess + agess*causes), (agess + agess*causes))
+
+    Ft[1:agess,1:agess] <- rbind(cbind(birth_female * Fft, birth_female * Fmt),
+                                 cbind((1-birth_female) * Fft, (1-birth_female) * Fmt))
+    Ft_star[1:agess,1:ages] <- rbind(birth_female * Fft, (1-birth_female) * Fft)
+    Fl[[as.character(years_data[t])]] <- Ft
+    Fl_star[[as.character(years_data[t])]] <- Ft_star
+    # parents age distribution under stable assumption in case no input
+    if(no_Pim | no_Pif){
+      A = Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
+      A_decomp = eigen(A)
+      lambda = as.double(A_decomp$values[1])
+      w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+      wf = w[1:ages]
+      wm = w[(ages+1):(2*ages)]
+      Pif[,t] = wf * ff[,t] / sum(wf * ff[,t])
+      Pim[,t] = wm * fm[,t] / sum(wm * fm[,t])
+    }
+
+    # project
+    Ut <- as.matrix(Ul[[t]])
+    Ft <- as.matrix(Fl[[t]])
+    Ft_star <- as.matrix(Fl_star[[t]])
+    pitf <- Pif[,t]
+    pitm <- Pim[,t]
+    pit <- c(pitf, pitm)
+    if (t==1){
+      p1f <- pf[,1]
+      p1m <- pm[,1]
+      f1f <- ff[,1]
+      f1m <- fm[,1]
+      pif1 <- Pif[,1]
+      pim1 <- Pim[,1]
+
+      # BEN: Add Hf and Hm
+      H1f <- Hf[[1]]
+      H1m <- Hm[[1]]
+
+      # BEN: cod version !!!
+      kin_all[[1]] <- kin_time_invariant_2sex_cod(pf = p1f, pm = p1m,
+                                                  ff = f1f, fm = f1m,
+                                                  pif = pif1, pim = pim1,
+                                                  Hf = H1f, Hm = H1m,
+                                              birth_female = birth_female, list_output = TRUE)
+    }
+    kin_all[[t+1]] <- timevarying_kin_2sex_cod(Ut=Ut, Ft=Ft, Ft_star=Ft_star, pit=pit, sex_focal, ages, pkin=kin_all[[t]])
+    pb$tick()
+  }
+
+  # filter years and kin that were selected
+  names(kin_all) <- as.character(years_data)
+
+  # combinations to return
+  out_selected <- output_period_cohort_combination(output_cohort, output_period, age = age, years_data = years_data)
+
+  possible_kin <- c("d","gd","ggd","m","gm","ggm","os","ys","nos","nys","oa","ya","coa","cya")
+  if(is.null(output_kin)){
+    selected_kin_position <- 1:length(possible_kin)
+  }else{
+    selected_kin_position <- which(possible_kin %in% output_kin)
+  }
+
+  # first filter
+  kin_list <- kin_all %>%
+    purrr::keep(names(.) %in% as.character(unique(out_selected$year))) %>%
+    purrr::map(~ .[selected_kin_position])
+  # long format
+  message("Preparing output...")
+  kin <- lapply(names(kin_list), FUN = function(Y){
+    X <- kin_list[[Y]]
+    X <- purrr::map2(X, names(X), function(x,y){
+      # reassign deaths to Focal experienced age
+      x[(agess+1):(agess + agess*causes),1:(ages-1)] <- x[(agess+1):(agess + agess*causes),2:ages]
+      x[(agess+1):(agess + agess*causes),ages] <- 0
+      x <- data.table::as.data.table(x)
+      x$year <- Y
+      x$kin <- y
+      x$sex_kin <- c(rep(c("f", "m"),each=ages), rep(c("f", "m"),each=ages*causes))
+      x$age_kin <- c(rep(age,2), rep(rep(age,each=causes),2))
+      x$alive <- c(rep("living",2*ages), rep(paste0("deadcause",1:causes),2*ages))
+      return(x)
+    }) %>%
+      data.table::rbindlist() %>%
+      stats::setNames(c(as.character(age), "year","kin","sex_kin","age_kin","alive")) %>%
+      data.table::melt(id.vars = c("year","kin","sex_kin","age_kin","alive"), variable.name = "age_focal", value.name = "count")
+    X$age_focal = as.integer(as.character(X$age_focal))
+    X$year = as.integer(X$year)
+    X$cohort = X$year - X$age_focal
+    X <- X[X$age_focal %in% out_selected$age[out_selected$year==as.integer(Y)],]
+    X <- data.table::dcast(X, year + kin + sex_kin + age_kin + age_focal + cohort ~ alive, value.var = "count", fun.aggregate = sum)
+  }) %>% data.table::rbindlist()
+
+  # results as list?
+  if(list_output) {
+    out <- kin_list
+  }else{
+    out <- kin
+  }
+  return(out)
+}
+
+#' one time projection kin
+
+#' @description one time projection kin. internal function.
+#'
+#' @param Ut numeric. A matrix of survival probabilities (or ratios).
+#' @param Ft numeric. A matrix of age-specific fertility rates.
+#' @param Ft_star numeric. Ft but for female fertility.
+#' @param pit numeric. A matrix with distribution of childbearing.
+#' @param sex_focal character. "f" for female or "m" for male.
+#' @param ages numeric.
+#' @param pkin numeric. A list with kin count distribution in previous year.
+#' @return A list of 14 types of kin matrices (kin age by Focal age, blocked for two sex) projected one time interval.
+#' @export
+timevarying_kin_2sex_cod<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
+
+  agess <- ages*2
+  om <- ages-1
+  pif <- pit[1:ages]
+  pim <- pit[(ages+1):agess]
+
+  # BEN: Changed dimensions of lower part (dead kin) to account for death from causes.
+  phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, (agess + agess*causes), ages)
+
+  kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
+                   nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
+
+  # G matrix moves focal by age
+  G <- matrix(0, nrow=ages, ncol=ages)
+  G[row(G)-1 == col(G)] <- 1
+
+  # BEN: Changed dimensions
+  Gt <- matrix(0, (agess + agess*causes), (agess + agess*causes))
+
+  Gt[1:(agess), 1:(agess)] <- as.matrix(Matrix::bdiag(G, G))
+
+  # locate focal at age 0 depending sex
+  sex_index <- ifelse(sex_focal == "f", 1, ages+1)
+  phi[sex_index, 1] <- 1
+
+  # BEN: NOT SURE ABOUT WHAT IS HAPPENING BELOW
+  # Rows are multiplied by the sum of the pi?
+
+  # initial distribution
+  m[1:agess,1]   = pit
+  gm[1:agess,1]  = pkin[["m"]][1:agess,] %*% (pif + pim)
+  ggm[1:agess,1] = pkin[["gm"]][1:agess,] %*% (pif + pim)
+  os[1:agess,1]  = pkin[["d"]][1:agess,] %*% pif
+  nos[1:agess,1] = pkin[["gd"]][1:ages,] %*% pif
+  oa[1:agess,1]  = pkin[["os"]][1:agess,] %*% (pif + pim)
+  ya[1:agess,1]  = pkin[["ys"]][1:agess,] %*% (pif + pim)
+  coa[1:agess,1] = pkin[["nos"]][1:agess,] %*% (pif + pim)
+  cya[1:agess,1] = pkin[["nys"]][1:agess,] %*% (pif + pim)
+
+  for (ix in 1:om){
+    phi[,ix+1] = Gt %*% phi[, ix]
+    d[,ix+1]   = Ut %*% pkin[["d"]][,ix]   + Ft %*% phi[,ix]
+    gd[,ix+1]  = Ut %*% pkin[["gd"]][,ix]  + Ft %*% pkin[["d"]][,ix]
+    ggd[,ix+1] = Ut %*% pkin[["ggd"]][,ix] + Ft %*% pkin[["gd"]][,ix]
+    m[,ix+1]   = Ut %*% pkin[["m"]][,ix]
+    gm[,ix+1]  = Ut %*% pkin[["gm"]][,ix]
+    ggm[,ix+1] = Ut %*% pkin[["ggm"]][,ix]
+    os[,ix+1]  = Ut %*% pkin[["os"]][,ix]
+    ys[,ix+1]  = Ut %*% pkin[["ys"]][,ix]  + Ft_star %*% pkin[["m"]][,ix]
+    nos[,ix+1] = Ut %*% pkin[["nos"]][,ix] + Ft %*% pkin[["os"]][,ix]
+    nys[,ix+1] = Ut %*% pkin[["nys"]][,ix] + Ft %*% pkin[["ys"]][,ix]
+    oa[,ix+1]  = Ut %*% pkin[["oa"]][,ix]
+    ya[,ix+1]  = Ut %*% pkin[["ya"]][,ix]  + Ft_star %*% pkin[["gm"]][,ix]
+    coa[,ix+1] = Ut %*% pkin[["coa"]][,ix] + Ft %*% pkin[["oa"]][,ix]
+    cya[,ix+1] = Ut %*% pkin[["cya"]][,ix] + Ft %*% pkin[["ya"]][,ix]
+  }
+
+  kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
+                   nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
+
+  return(kin_list)
+}

From 725e1736328c540ccef52ba16e9c545d07ae443b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Benjamin-Samuel=20Schl=C3=BCter?=
 <benjamin-samuel.schluter@uclouvain.be>
Date: Tue, 20 Feb 2024 13:04:02 -0500
Subject: [PATCH 32/89] script to compare functions with/without cod

---
 R/kin_time_variant_2sex_cod.R | 79 ++++++++++++++++----------------
 R/test.R                      | 85 +++++++++++++++++++++++++++++++++++
 2 files changed, 126 insertions(+), 38 deletions(-)
 create mode 100644 R/test.R

diff --git a/R/kin_time_variant_2sex_cod.R b/R/kin_time_variant_2sex_cod.R
index 0d32652..be3416f 100644
--- a/R/kin_time_variant_2sex_cod.R
+++ b/R/kin_time_variant_2sex_cod.R
@@ -24,44 +24,44 @@
 
 ## BEN: ========================================================================
 # Function building:
-library(DemoKin)
-library(tidyr)
-library(dplyr)
-library(here)
-
-# Input of model
-years <- ncol(swe_px)
-ages <- nrow(swe_px)
-ff <- swe_asfr
-fm <- rbind(matrix(0, 5, years),
-            swe_asfr[-((ages-4):ages),]) * 1.05
-pf <- swe_px
-pm <- swe_px ^ 1.5
-
-sex_focal = "f"
-time_invariant = FALSE
-birth_female = .5
-output_cohort = 1900 # like in the vignette
-output_period = NULL
-output_kin = NULL
-
-
-pif <- pim <- NULL
-nf <- nm <- NULL
-list_output = FALSE
-
-# Create a fictitious hazard matrix with three causes of death, where each
-# year is a list item (Hazard matrix needs to be (causes * ages) for the
-# matrix algebra to work well with existing code).
-H <- matrix(c(0.5, 1, 2), nrow = 3, ncol = nrow(pf))
-Hf <- Hm <- sapply(colnames(pf), function(x) {
-  return(H)
-  },
-  simplify = FALSE,
-  USE.NAMES = TRUE
-  )
-# BEN: Load time invariant for COD
-source("./R/kin_time_invariant_2sex_cod.R")
+# library(DemoKin)
+# library(tidyr)
+# library(dplyr)
+# library(here)
+#
+# # Input of model
+# years <- ncol(swe_px)
+# ages <- nrow(swe_px)
+# ff <- swe_asfr
+# fm <- rbind(matrix(0, 5, years),
+#             swe_asfr[-((ages-4):ages),]) * 1.05
+# pf <- swe_px
+# pm <- swe_px ^ 1.5
+#
+# sex_focal = "f"
+# time_invariant = FALSE
+# birth_female = .5
+# output_cohort = 1900 # like in the vignette
+# output_period = NULL
+# output_kin = NULL
+#
+#
+# pif <- pim <- NULL
+# nf <- nm <- NULL
+# list_output = FALSE
+#
+# # Create a fictitious hazard matrix with three causes of death, where each
+# # year is a list item (Hazard matrix needs to be (causes * ages) for the
+# # matrix algebra to work well with existing code).
+# H <- matrix(c(0.5, 1, 2), nrow = 3, ncol = nrow(pf))
+# Hf <- Hm <- sapply(colnames(pf), function(x) {
+#   return(H)
+#   },
+#   simplify = FALSE,
+#   USE.NAMES = TRUE
+#   )
+# # BEN: Load time invariant for COD
+# source("./R/kin_time_invariant_2sex_cod.R")
 
 
 ## =============================================================================
@@ -298,6 +298,9 @@ timevarying_kin_2sex_cod<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
   pif <- pit[1:ages]
   pim <- pit[(ages+1):agess]
 
+  # BEN : Add the number of CoD
+  causes <- nrow(Hf[[1]])
+
   # BEN: Changed dimensions of lower part (dead kin) to account for death from causes.
   phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, (agess + agess*causes), ages)
 
diff --git a/R/test.R b/R/test.R
new file mode 100644
index 0000000..b593dea
--- /dev/null
+++ b/R/test.R
@@ -0,0 +1,85 @@
+
+rm(list = ls())
+
+## Compare output
+
+library(DemoKin)
+
+source("./R/kin_time_invariant_2sex_cod.R")
+source("./R/kin_time_variant_2sex_cod.R")
+
+source("./R/kin_time_invariant_2sex.R")
+source("./R/kin_time_variant_2sex.R")
+
+## Example from Vignette 2 sex but few years for speed
+
+years_all <- ncol(swe_px)
+years_test <- 1:20
+ages <- nrow(swe_px)
+pf <- swe_px[,years_test]
+pm <- swe_px[,years_test] ^ 1.5 # artificial perturbation for this example
+ff <- swe_asfr[, years_test]
+fm <- rbind(matrix(0, 5, max(years_test)),
+                           swe_asfr[-((ages-4):ages), years_test]) * 1.05 # artificial perturbation for this example
+
+# Create a fictitious hazard matrix with three causes of death, where each
+# year is a list item (Hazard matrix needs to be (causes * ages) for the
+# matrix algebra to work well with existing code).
+H <- matrix(c(0.5, 1, 2), nrow = 3, ncol = nrow(pf))
+Hf <- Hm <- sapply(colnames(pf), function(x) {
+  return(H)
+  },
+  simplify = FALSE,
+  USE.NAMES = TRUE
+  )
+
+## COMPARISON
+
+start.time <- Sys.time()
+no_cod <- kin_time_variant_2sex(pf = pf, pm = pm,
+                      ff = ff, fm = fm,
+                      sex_focal = "f",
+                      birth_female = 1/2.04,
+                      pif = NULL, pim = NULL,
+                      nf = NULL, nm = NULL,
+                      output_cohort = NULL, output_period = NULL, output_kin = NULL,
+                      list_output = FALSE)
+end.time <- Sys.time()
+time.taken.no.cod <- end.time - start.time
+
+
+start.time <- Sys.time()
+cod <- kin_time_variant_2sex_cod(pf = pf, pm = pm,
+                                 ff = ff, fm = fm,
+                                 Hf = Hf, Hm = Hm,
+                                 sex_focal = "f",
+                                 birth_female = 1/2.04,
+                                 pif = NULL, pim = NULL,
+                                 nf = NULL, nm = NULL,
+                                 output_cohort = NULL, output_period = NULL, output_kin = NULL,
+                                 list_output = FALSE)
+end.time <- Sys.time()
+time.taken.cod <- end.time - start.time
+
+no_cod
+cod |>
+  mutate(
+    dead = deadcause1 + deadcause2 + deadcause3
+    )
+
+
+no_cod |>
+  filter(
+    kin == "gm",
+    age_focal %in% 30:35,
+    age_kin > 60
+  )
+cod |>
+  mutate(
+    dead = deadcause1 + deadcause2 + deadcause3
+    ) |>
+  filter(
+    kin == "gm",
+    age_focal %in% 30:35,
+    age_kin > 60
+  )

From 29ecf60c1a9a47dc8be25f99f166c183517868db Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Benjamin-Samuel=20Schl=C3=BCter?=
 <benjamin-samuel.schluter@uclouvain.be>
Date: Wed, 21 Feb 2024 14:14:05 -0500
Subject: [PATCH 33/89] add description in top of scripts

---
 R/kin_time_invariant_2sex_cod.R | 7 +++++--
 R/kin_time_variant_2sex_cod.R   | 5 ++++-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/R/kin_time_invariant_2sex_cod.R b/R/kin_time_invariant_2sex_cod.R
index 0434360..e25ad27 100644
--- a/R/kin_time_invariant_2sex_cod.R
+++ b/R/kin_time_invariant_2sex_cod.R
@@ -1,13 +1,16 @@
 #' Estimate kin counts in a time invariant framework for two-sex model.
 
-#' @description Two-sex matrix framework for kin count estimates.This produces kin counts grouped by kin, age and sex of
+#' @description Two-sex matrix framework for kin count and death estimates.This produces kin counts grouped by kin, age and sex of
 #' each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
-#' are grouped in one male count of cousins.
+#' are grouped in one male count of cousins. This also produces kin deaths grouped by kin, age, sex of
+#' each relatives at each Focal´s age, and cause of death.
 #' @details See Caswell (2022) for details on formulas.
 #' @param pf numeric. A vector of survival probabilities for females with same length as ages.
+#' @param Hf numeric. A matrix with cause-specific hazards for females with rows as causes and columns as ages, being the name of each col the age.
 #' @param ff numeric. A vector of age-specific fertility rates for females with same length as ages.
 #' @param pm numeric. A vector of survival probabilities for males with same length as ages.
 #' @param fm numeric. A vector of age-specific fertility rates for males with same length as ages.
+#' @param Hm numeric. A matrix with cause-specific hazards for males with rows as causes and columns as ages, being the name of each col the age.
 #' @param sex_focal character. "f" for female or "m" for male.
 #' @param birth_female numeric. Female portion at birth.
 #' @param pif numeric. For using some specific non-stable age distribution of childbearing for mothers (same length as ages). Default `NULL`.
diff --git a/R/kin_time_variant_2sex_cod.R b/R/kin_time_variant_2sex_cod.R
index be3416f..52caad6 100644
--- a/R/kin_time_variant_2sex_cod.R
+++ b/R/kin_time_variant_2sex_cod.R
@@ -2,12 +2,15 @@
 
 #' @description Two-sex matrix framework for kin count estimates with varying rates.
 #' This produces kin counts grouped by kin, age and sex of each relatives at each Focal´s age.
-#' For example, male cousins from aunts and uncles from different sibling's parents are grouped in one male count of cousins.
+#' For example, male cousins from aunts and uncles from different sibling's parents are grouped in one male count of cousins. This also produces kin deaths grouped by kin, age, sex of
+#' each relatives at each Focal´s age, and cause of death.
 #' @details See Caswell (2022) for details on formulas.
 #' @param pf numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
 #' @param pm numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).
 #' @param ff numeric. Same as pf but for fertility rates.
 #' @param fm numeric. Same as pm but for fertility rates.
+#' @param Hf numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for females with rows as causes and columns as ages, being the name of each col the age.
+#' @param Hm numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for males with rows as causes and columns as ages, being the name of each col the age.
 #' @param sex_focal character. "f" for female or "m" for male.
 #' @param pif numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default `NULL`.
 #' @param pim numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default `NULL`.

From fe64c82177bb0198337453047cf0de8707759cf1 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 8 Mar 2024 11:30:02 -0300
Subject: [PATCH 34/89] issues pending

---
 CRAN-SUBMISSION             |   4 +-
 R/kin.R                     |  72 ++++++++++++++++++------------------
 R/kin2sex.R                 |  52 ++++++++++++++++----------
 R/kin_time_invariant_2sex.R |   2 +
 R/kin_time_variant_2sex.R   |   9 +++--
 R/plot_diagramm.R           |   2 +-
 data/demokin_codes.rda      | Bin 607 -> 611 bytes
 7 files changed, 78 insertions(+), 63 deletions(-)

diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index 90dd290..04b6fae 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 1.0.3
-Date: 2023-05-26 19:38:45 UTC
-SHA: b3f5387a20bd765d8567e6cfa6a8918413b1e138
+Date: 2023-06-04 13:27:38 UTC
+SHA: f54cd023b2e6bb2de1e74d1d0c89c13828149c44
diff --git a/R/kin.R b/R/kin.R
index 0d5a060..ac782c2 100644
--- a/R/kin.R
+++ b/R/kin.R
@@ -12,10 +12,10 @@
 #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
+#' @param output_age_focal integer. Vector of ages to select (and make faster the run).
 #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring,
+#' @param summary_kin logical. Whether or not include `kin_summary` table (see output details). Default `TRUE`.
 #' this needs to be set as 1.
-#' @param stable logic. Deprecated. Use `time_invariant`.
-#' @param U logic. Deprecated. Use `p`.
 #' @return A list with:
 #' \itemize{
 #'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` is daughter,
@@ -44,25 +44,14 @@
 kin <- function(p = NULL, f = NULL,
                 time_invariant = TRUE,
                 pi = NULL, n = NULL,
-                output_cohort = NULL, output_period = NULL, output_kin=NULL,
+                output_cohort = NULL, output_period = NULL, output_kin=NULL, output_age_focal = NULL,
                 birth_female = 1/2.04,
-                stable = lifecycle::deprecated(),
-                U = lifecycle::deprecated())
+                summary_kin = TRUE)
   {
 
   # global vars
   living<-dead<-age_kin<-age_focal<-cohort<-year<-total<-mean_age<-count_living<-sd_age<-count_dead<-mean_age_lost<-indicator<-value<-NULL
 
-  # changed arguments
-  if (lifecycle::is_present(stable)) {
-    lifecycle::deprecate_warn("0.0.0.9000", "kin(stable)", details = "Used time_invariant")
-    time_invariant <- stable
-  }
-  if (lifecycle::is_present(U)) {
-    lifecycle::deprecate_warn("0.0.0.9000", "kin(stable)", details = "Used time_invariant")
-    p <- U
-  }
-
   # kin to return
   all_possible_kin <- c("coa", "cya", "d", "gd", "ggd", "ggm", "gm", "m", "nos", "nys", "oa", "ya", "os", "ys")
   output_kin_asked <- output_kin
@@ -109,7 +98,10 @@ kin <- function(p = NULL, f = NULL,
                        .by = c(kin, age_kin, age_focal, cohort, year))
   }
 
-  # select period/cohort
+  # select period/cohort/age
+  if(!is.null(output_age_focal) & all(output_age_focal %in% 1:120)){
+    kin_full <- kin_full %>% dplyr::filter(age_focal %in% output_age_focal)
+  }
   if(!is.null(output_cohort)){
     agrupar <- "cohort"
   } else if(!is.null(output_period)){
@@ -120,29 +112,35 @@ kin <- function(p = NULL, f = NULL,
   agrupar_no_age_focal <- c("kin", agrupar)
   agrupar <- c("age_focal", "kin", agrupar)
 
-  # get summary indicators based on group variables
-  kin_summary <- dplyr::bind_rows(
-    kin_full %>%
-      dplyr::rename(total=living) %>%
-      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
-      dplyr::summarise(count_living = sum(total),
-                mean_age = sum(total*age_kin)/sum(total),
-                sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
-      tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", values_to = "value"),
-    kin_full %>%
-      dplyr::rename(total=dead) %>%
-      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
-      dplyr::summarise(count_dead = sum(total)) %>%
-      dplyr::ungroup() %>%
-      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar_no_age_focal))) %>%
-      dplyr::mutate(count_cum_dead = cumsum(count_dead),
-                    mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>%
-      dplyr::ungroup() %>%
-      tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", values_to = "value")) %>%
+  # get summary indicators based on group variables. If it is asked
+  if(summary_kin){
+    kin_summary <- dplyr::bind_rows(
+      kin_full %>%
+        dplyr::rename(total=living) %>%
+        dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
+        dplyr::summarise(count_living = sum(total),
+                         mean_age = sum(total*age_kin)/sum(total),
+                         sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
+        tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", values_to = "value"),
+      kin_full %>%
+        dplyr::rename(total=dead) %>%
+        dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
+        dplyr::summarise(count_dead = sum(total)) %>%
+        dplyr::ungroup() %>%
+        dplyr::group_by(dplyr::across(dplyr::all_of(agrupar_no_age_focal))) %>%
+        dplyr::mutate(count_cum_dead = cumsum(count_dead),
+                      mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>%
+        dplyr::ungroup() %>%
+        tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", values_to = "value")) %>%
       dplyr::ungroup() %>%
       tidyr::pivot_wider(names_from = indicator, values_from = value)
 
-  # return
-  kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
+    # return
+    kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
+  }else{
+    # return
+    kin_out <- kin_full
+  }
+
   return(kin_out)
 }
diff --git a/R/kin2sex.R b/R/kin2sex.R
index a50a438..9ce365a 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -18,7 +18,9 @@
 #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
 #' @param output_period integer. Vector of period years for returning results. Should be within input data years range.
 #' @param output_kin character. kin types to return: "m" for mother, "d" for daughter,...
+#' @param output_age_focal integer. Vector of ages to select (and make faster the run).
 #' @param birth_female numeric. Female portion at birth. This multiplies `f` argument. If `f` is already for female offspring, this needs to be set as 1.
+#' @param summary_kin logical. Whether or not include `kin_summary` table (see output details). Default `TRUE`.
 #' @return A list with:
 #' \itemize{
 #'  \item{kin_full}{ a data frame with year, cohort, Focal´s age, related ages and type of kin (for example `d` could be daughter or son depending `sex_kin`,
@@ -51,7 +53,8 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
                  birth_female = 1/2.04,
                  pif = NULL, pim = NULL,
                  nf = NULL, nm = NULL,
-                 output_cohort = NULL, output_period = NULL, output_kin=NULL)
+                 output_cohort = NULL, output_period = NULL, output_kin=NULL,output_age_focal = NULL,
+                 summary_kin = TRUE)
   {
 
   # global vars
@@ -116,7 +119,10 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   }
 
   # summary
-  # select period/cohort
+  # select period/cohort/ge
+  if(!is.null(output_age_focal) & all(output_age_focal %in% 1:120)){
+    kin_full <- kin_full %>% dplyr::filter(age_focal %in% output_age_focal)
+  }
   if(!is.null(output_cohort)){
     agrupar <- "cohort"
   } else if(!is.null(output_period)){
@@ -127,28 +133,34 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   agrupar_no_age_focal <- c("kin", "sex_kin", agrupar)
   agrupar <- c("age_focal", "kin", "sex_kin", agrupar)
 
-  kin_summary <- dplyr::bind_rows(
-    as.data.frame(kin_full) %>%
-      dplyr::rename(total=living) %>%
-      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
-      dplyr::summarise(count_living = sum(total),
-                mean_age = sum(total*age_kin)/sum(total),
-                sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
-      tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", values_to = "value"),
-    as.data.frame(kin_full) %>%
-      dplyr::rename(total=dead) %>%
-      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
-      dplyr::summarise(count_dead = sum(total)) %>%
-      dplyr::ungroup() %>%
-      dplyr::group_by(dplyr::across(dplyr::all_of(agrupar_no_age_focal))) %>%
-      dplyr::mutate(count_cum_dead = cumsum(count_dead),
-                    mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>%
-      dplyr::ungroup() %>%
-      tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", values_to = "value")) %>%
+  if(summary_kin){
+    kin_summary <- dplyr::bind_rows(
+      as.data.frame(kin_full) %>%
+        dplyr::rename(total=living) %>%
+        dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
+        dplyr::summarise(count_living = sum(total),
+                         mean_age = sum(total*age_kin)/sum(total),
+                         sd_age  = (sum(total*age_kin^2)/sum(total)-mean_age^2)^.5) %>%
+        tidyr::pivot_longer(count_living:sd_age, names_to = "indicator", values_to = "value"),
+      as.data.frame(kin_full) %>%
+        dplyr::rename(total=dead) %>%
+        dplyr::group_by(dplyr::across(dplyr::all_of(agrupar))) %>%
+        dplyr::summarise(count_dead = sum(total)) %>%
+        dplyr::ungroup() %>%
+        dplyr::group_by(dplyr::across(dplyr::all_of(agrupar_no_age_focal))) %>%
+        dplyr::mutate(count_cum_dead = cumsum(count_dead),
+                      mean_age_lost = cumsum(count_dead * age_focal)/cumsum(count_dead)) %>%
+        dplyr::ungroup() %>%
+        tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", values_to = "value")) %>%
       dplyr::ungroup() %>%
       tidyr::pivot_wider(names_from = indicator, values_from = value)
 
     # return
     kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
+  }else{
+    # return
+    kin_out <- kin_full
+  }
+
   return(kin_out)
 }
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index 07628e1..e9d7822 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -105,6 +105,8 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
     ggm[,i+1]  = Ut %*% ggm[,i]
   }
 
+  # atribuible to focal sex
+  pios = if(sex_focal == "f") pif else pim
   os[1:(agess),1]  = d[1:(agess),] %*% pif
   nos[1:(agess),1] = gd[1:(agess),] %*% pif
   for(i in 1:(ages-1)){
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index 83b3956..e67db6f 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -123,6 +123,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
       pim1 <- Pim[,1]
       kin_all[[1]] <- kin_time_invariant_2sex(pf = p1f, pm = p1m,
                                               ff = f1f, fm = f1m,
+                                              sex_focal = sex_focal,
                                               pif = pif1, pim = pim1,
                                               birth_female = birth_female, list_output = TRUE)
     }
@@ -148,7 +149,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
     purrr::keep(names(.) %in% as.character(unique(out_selected$year))) %>%
     purrr::map(~ .[selected_kin_position])
   # long format
-  message("Preparing output...")
+  message(" Preparing output...")
   kin <- lapply(names(kin_list), FUN = function(Y){
     X <- kin_list[[Y]]
     X <- purrr::map2(X, names(X), function(x,y){
@@ -219,12 +220,14 @@ timevarying_kin_2sex<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
   m[1:agess,1]   = pit
   gm[1:agess,1]  = pkin[["m"]][1:agess,] %*% (pif + pim)
   ggm[1:agess,1] = pkin[["gm"]][1:agess,] %*% (pif + pim)
-  os[1:agess,1]  = pkin[["d"]][1:agess,] %*% pif
-  nos[1:agess,1] = pkin[["gd"]][1:ages,] %*% pif
   oa[1:agess,1]  = pkin[["os"]][1:agess,] %*% (pif + pim)
   ya[1:agess,1]  = pkin[["ys"]][1:agess,] %*% (pif + pim)
   coa[1:agess,1] = pkin[["nos"]][1:agess,] %*% (pif + pim)
   cya[1:agess,1] = pkin[["nys"]][1:agess,] %*% (pif + pim)
+  # atribuible to focal sex
+  pios = if(sex_focal == "f") pif else pim
+  os[1:agess,1]  = pkin[["d"]][1:agess,] %*% pios
+  nos[1:agess,1] = pkin[["gd"]][1:ages,] %*% pios
 
   for (ix in 1:om){
     phi[,ix+1] = Gt %*% phi[, ix]
diff --git a/R/plot_diagramm.R b/R/plot_diagramm.R
index f1750f1..6bffa8c 100644
--- a/R/plot_diagramm.R
+++ b/R/plot_diagramm.R
@@ -10,7 +10,7 @@ plot_diagram <-
   function (kin_total, rounding = 3) {
     rels <- c("ggd", "gd", "d", "Focal", "m", "gm", "ggm", "oa", "coa", "os", "nos", "ya", "cya", "ys", "nys")
     # check all types are in
-    if(!any(unique(kin_total$kin) %in% rels)) stop("You need all specific types. If some are missed or grouped, for example old and younger sisters in 's', this will fail.")
+    if(!any(unique(kin_total$kin) %in% rels) | any(c("s", "c", "a", "n") %in% unique(kin_total$kin))) stop("You need all specific types. If some are missed or grouped, for example old and younger sisters in 's', this will fail.")
     vertices <- data.frame(
       nodes = rels
       , x = c(1, 1, 1, 1, 1, 1, 1, 0, -1, 0, -1, 2, 3, 2, 3)
diff --git a/data/demokin_codes.rda b/data/demokin_codes.rda
index 3859ab313ac97b245ae997bc2c4d20df6deaf116..8ab66208184eae90ec3e617e2c20e26b8717be18 100644
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From bd626f204fe6632b6964efa2047c4bc2b15a78e6 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Mon, 18 Mar 2024 15:51:00 -0300
Subject: [PATCH 35/89] adding cod and vignette

---
 NAMESPACE                          |  3 ++
 R/kin2sex.R                        | 68 +++++++++++++++++-------
 R/kin_time_invariant_2sex_cod.R    | 25 ---------
 R/kin_time_variant_2sex_cod.R      | 46 ----------------
 R/test.R                           | 85 ------------------------------
 man/kin.Rd                         | 12 ++---
 man/kin2sex.Rd                     | 14 ++++-
 man/kin_time_invariant_2sex_cod.Rd | 59 +++++++++++++++++++++
 man/kin_time_variant_2sex_cod.Rd   | 70 ++++++++++++++++++++++++
 man/timevarying_kin_2sex_cod.Rd    | 29 ++++++++++
 vignettes/Reference_TwoSex.Rmd     | 41 +++++++++++++-
 vignettes/references.bib           | 12 +++++
 12 files changed, 280 insertions(+), 184 deletions(-)
 delete mode 100644 R/test.R
 create mode 100644 man/kin_time_invariant_2sex_cod.Rd
 create mode 100644 man/kin_time_variant_2sex_cod.Rd
 create mode 100644 man/timevarying_kin_2sex_cod.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 0d95065..9a087d8 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -6,11 +6,14 @@ export(kin2sex)
 export(kin_multi_stage)
 export(kin_time_invariant)
 export(kin_time_invariant_2sex)
+export(kin_time_invariant_2sex_cod)
 export(kin_time_variant)
 export(kin_time_variant_2sex)
+export(kin_time_variant_2sex_cod)
 export(output_period_cohort_combination)
 export(plot_diagram)
 export(rename_kin)
 export(timevarying_kin)
 export(timevarying_kin_2sex)
+export(timevarying_kin_2sex_cod)
 importFrom(magrittr,"%>%")
diff --git a/R/kin2sex.R b/R/kin2sex.R
index 9ce365a..07dfa11 100644
--- a/R/kin2sex.R
+++ b/R/kin2sex.R
@@ -13,6 +13,8 @@
 #' @param sex_focal character. "f" for female or "m" for male.
 #' @param pif numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default `NULL`.
 #' @param pim numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default `NULL`.
+#' @param Hf numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for females with rows as causes and columns as ages, being the name of each col the age.
+#' @param Hm numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for males with rows as causes and columns as ages, being the name of each col the age.
 #' @param nf numeric. Only for `time_invariant = FALSE`. Same as `pf` but for population distribution (counts or `%`). Optional.
 #' @param nm numeric. Only for `time_invariant = FALSE`. Same as `pm` but for population distribution (counts or `%`). Optional.
 #' @param output_cohort integer. Vector of year cohorts for returning results. Should be within input data years range.
@@ -53,6 +55,7 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
                  birth_female = 1/2.04,
                  pif = NULL, pim = NULL,
                  nf = NULL, nm = NULL,
+                 Hf = NULL, Hm = NULL,
                  output_cohort = NULL, output_period = NULL, output_kin=NULL,output_age_focal = NULL,
                  summary_kin = TRUE)
   {
@@ -76,6 +79,9 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
     output_kin <- match.arg(tolower(output_kin), all_possible_kin, several.ok = TRUE)
   }
 
+  # is cause of death specific or not
+  is_cod <- !is.null(Hf) & !is.null(Hm)
+
   # if time dependent or not
   if(time_invariant){
       if(!is.vector(pf)) {
@@ -85,27 +91,50 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
         ff <- ff[,as.character(output_period)]
         fm <- fm[,as.character(output_period)]
       }
-      kin_full <- kin_time_invariant_2sex(pf, pm, ff, fm,
-                                     sex_focal = sex_focal,
-                                     birth_female = birth_female,
-                                     pif = pif, pim = pim,
-                                     output_kin = output_kin) %>%
-                              dplyr::mutate(cohort = NA, year = NA)
+      if(is_cod){
+        kin_full <- kin_time_invariant_2sex_cod(pf, pm, ff, fm,
+                                            sex_focal = sex_focal,
+                                            birth_female = birth_female,
+                                            pif = pif, pim = pim,
+                                            Hf = Hf, Hm = Hm,
+                                            output_kin = output_kin) %>%
+          dplyr::mutate(cohort = NA, year = NA)
+      }else{
+        kin_full <- kin_time_invariant_2sex(pf, pm, ff, fm,
+                                            sex_focal = sex_focal,
+                                            birth_female = birth_female,
+                                            pif = pif, pim = pim,
+                                            output_kin = output_kin) %>%
+          dplyr::mutate(cohort = NA, year = NA)
+      }
+
   }else{
       if(!is.null(output_cohort) & !is.null(output_period)) stop("sorry, you can not select cohort and period. Choose one please")
+    if(is_cod){
+      kin_full <- kin_time_variant_2sex_cod(pf = pf, pm = pm,
+                                        ff = ff, fm = fm,
+                                        sex_focal = sex_focal,
+                                        birth_female = birth_female,
+                                        pif = pif, pim = pim,
+                                        nf = nf, nm = nm,
+                                        Hf = Hf, Hm = Hm,
+                                        output_cohort = output_cohort, output_period = output_period,
+                                        output_kin = output_kin)
+    }else{
       kin_full <- kin_time_variant_2sex(pf = pf, pm = pm,
-                                   ff = ff, fm = fm,
-                                   sex_focal = sex_focal,
-                                   birth_female = birth_female,
-                                   pif = pif, pim = pim,
-                                   nf = nf, nm = nm,
-                                   output_cohort = output_cohort, output_period = output_period,
-                                   output_kin = output_kin)
+                                        ff = ff, fm = fm,
+                                        sex_focal = sex_focal,
+                                        birth_female = birth_female,
+                                        pif = pif, pim = pim,
+                                        nf = nf, nm = nm,
+                                        output_cohort = output_cohort, output_period = output_period,
+                                        output_kin = output_kin)
+    }
       message(paste0("Assuming stable population before ", min(years_data), "."))
   }
 
   # reorder
-  kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, dead)
+  kin_full <- kin_full %>% dplyr::select(year, cohort, age_focal, sex_kin, kin, age_kin, living, starts_with("dea"))
 
   # re-group if grouped type is asked
   if(!is.null(output_kin_asked) & length(output_kin_asked)!=length(output_kin)){
@@ -114,8 +143,9 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
     if("a" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("oa", "ya")]   <- "a"
     if("n" %in% output_kin_asked) kin_full$kin[kin_full$kin %in% c("nos", "nys")] <- "n"
     kin_full <- kin_full %>%
-      dplyr::summarise(living = sum(living), dead = sum(dead),
-                       .by = c(kin, age_kin, age_focal, sex_kin, cohort, year))
+      dplyr::group_by(kin, age_kin, age_focal, sex_kin, cohort, year) %>%
+      dplyr::summarise_at(vars(c("living", dplyr::starts_with("dea"))), funs(sum)) %>%
+      dplyr::ungroup()
   }
 
   # summary
@@ -133,7 +163,8 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
   agrupar_no_age_focal <- c("kin", "sex_kin", agrupar)
   agrupar <- c("age_focal", "kin", "sex_kin", agrupar)
 
-  if(summary_kin){
+  # only return summary if is asked and is not cod
+  if(summary_kin & !is_cod){
     kin_summary <- dplyr::bind_rows(
       as.data.frame(kin_full) %>%
         dplyr::rename(total=living) %>%
@@ -154,11 +185,8 @@ kin2sex <- function(pf = NULL, pm = NULL, ff = NULL, fm = NULL,
         tidyr::pivot_longer(count_dead:mean_age_lost, names_to = "indicator", values_to = "value")) %>%
       dplyr::ungroup() %>%
       tidyr::pivot_wider(names_from = indicator, values_from = value)
-
-    # return
     kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)
   }else{
-    # return
     kin_out <- kin_full
   }
 
diff --git a/R/kin_time_invariant_2sex_cod.R b/R/kin_time_invariant_2sex_cod.R
index e25ad27..e0d17d6 100644
--- a/R/kin_time_invariant_2sex_cod.R
+++ b/R/kin_time_invariant_2sex_cod.R
@@ -22,31 +22,6 @@
 #' (for example `d` is children, `oa` is older aunts/uncles, etc.), sex, alive and death. If `list_output = TRUE` then this is a list.
 #' @export
 
-## BEN: ========================================================================
-# Function building:
-# library(DemoKin)
-# library(tidyr)
-# library(dplyr)
-# library(here)
-#
-# # Input of model
-# ff <- fra_asfr_sex[,"ff"]
-# fm <- fra_asfr_sex[,"fm"]
-# pf <- fra_surv_sex[,"pf"]
-# pm <- fra_surv_sex[,"pm"]
-# birth_female = 1/2.04
-# pif <- pim <- NULL
-# sex_focal = "f"
-# output_kin = NULL
-# list_output = FALSE
-#
-# # Create a fictitious hazard matrix with three causes of death.
-# # Assume that each cause consists of 1/3 of all death in all age groups.
-# Hf <- Hm <- matrix(1, nrow = 3, ncol = length(ff))
-
-## =============================================================================
-
-
 # BEN: Added hazard matrices as inputs.
 #      Assume that input of cause-specific mortality will be in terms of
 #      matrices of cause-specific hazards for the two sexes (causes * ages).
diff --git a/R/kin_time_variant_2sex_cod.R b/R/kin_time_variant_2sex_cod.R
index 52caad6..562be20 100644
--- a/R/kin_time_variant_2sex_cod.R
+++ b/R/kin_time_variant_2sex_cod.R
@@ -24,52 +24,6 @@
 #' @return A data.frame with year, cohort, Focal´s age, related ages, sex and type of kin (for example `d` is daughter, `oa` is older aunts, etc.), including living and dead kin at that age and sex.
 #' @export
 
-
-## BEN: ========================================================================
-# Function building:
-# library(DemoKin)
-# library(tidyr)
-# library(dplyr)
-# library(here)
-#
-# # Input of model
-# years <- ncol(swe_px)
-# ages <- nrow(swe_px)
-# ff <- swe_asfr
-# fm <- rbind(matrix(0, 5, years),
-#             swe_asfr[-((ages-4):ages),]) * 1.05
-# pf <- swe_px
-# pm <- swe_px ^ 1.5
-#
-# sex_focal = "f"
-# time_invariant = FALSE
-# birth_female = .5
-# output_cohort = 1900 # like in the vignette
-# output_period = NULL
-# output_kin = NULL
-#
-#
-# pif <- pim <- NULL
-# nf <- nm <- NULL
-# list_output = FALSE
-#
-# # Create a fictitious hazard matrix with three causes of death, where each
-# # year is a list item (Hazard matrix needs to be (causes * ages) for the
-# # matrix algebra to work well with existing code).
-# H <- matrix(c(0.5, 1, 2), nrow = 3, ncol = nrow(pf))
-# Hf <- Hm <- sapply(colnames(pf), function(x) {
-#   return(H)
-#   },
-#   simplify = FALSE,
-#   USE.NAMES = TRUE
-#   )
-# # BEN: Load time invariant for COD
-# source("./R/kin_time_invariant_2sex_cod.R")
-
-
-## =============================================================================
-
-
 # BEN: Added hazard matrices as inputs.
 #      Assume that input of cause-specific mortality will be in terms of
 #      matrices of cause-specific hazards for the two sexes (causes * ages).
diff --git a/R/test.R b/R/test.R
deleted file mode 100644
index b593dea..0000000
--- a/R/test.R
+++ /dev/null
@@ -1,85 +0,0 @@
-
-rm(list = ls())
-
-## Compare output
-
-library(DemoKin)
-
-source("./R/kin_time_invariant_2sex_cod.R")
-source("./R/kin_time_variant_2sex_cod.R")
-
-source("./R/kin_time_invariant_2sex.R")
-source("./R/kin_time_variant_2sex.R")
-
-## Example from Vignette 2 sex but few years for speed
-
-years_all <- ncol(swe_px)
-years_test <- 1:20
-ages <- nrow(swe_px)
-pf <- swe_px[,years_test]
-pm <- swe_px[,years_test] ^ 1.5 # artificial perturbation for this example
-ff <- swe_asfr[, years_test]
-fm <- rbind(matrix(0, 5, max(years_test)),
-                           swe_asfr[-((ages-4):ages), years_test]) * 1.05 # artificial perturbation for this example
-
-# Create a fictitious hazard matrix with three causes of death, where each
-# year is a list item (Hazard matrix needs to be (causes * ages) for the
-# matrix algebra to work well with existing code).
-H <- matrix(c(0.5, 1, 2), nrow = 3, ncol = nrow(pf))
-Hf <- Hm <- sapply(colnames(pf), function(x) {
-  return(H)
-  },
-  simplify = FALSE,
-  USE.NAMES = TRUE
-  )
-
-## COMPARISON
-
-start.time <- Sys.time()
-no_cod <- kin_time_variant_2sex(pf = pf, pm = pm,
-                      ff = ff, fm = fm,
-                      sex_focal = "f",
-                      birth_female = 1/2.04,
-                      pif = NULL, pim = NULL,
-                      nf = NULL, nm = NULL,
-                      output_cohort = NULL, output_period = NULL, output_kin = NULL,
-                      list_output = FALSE)
-end.time <- Sys.time()
-time.taken.no.cod <- end.time - start.time
-
-
-start.time <- Sys.time()
-cod <- kin_time_variant_2sex_cod(pf = pf, pm = pm,
-                                 ff = ff, fm = fm,
-                                 Hf = Hf, Hm = Hm,
-                                 sex_focal = "f",
-                                 birth_female = 1/2.04,
-                                 pif = NULL, pim = NULL,
-                                 nf = NULL, nm = NULL,
-                                 output_cohort = NULL, output_period = NULL, output_kin = NULL,
-                                 list_output = FALSE)
-end.time <- Sys.time()
-time.taken.cod <- end.time - start.time
-
-no_cod
-cod |>
-  mutate(
-    dead = deadcause1 + deadcause2 + deadcause3
-    )
-
-
-no_cod |>
-  filter(
-    kin == "gm",
-    age_focal %in% 30:35,
-    age_kin > 60
-  )
-cod |>
-  mutate(
-    dead = deadcause1 + deadcause2 + deadcause3
-    ) |>
-  filter(
-    kin == "gm",
-    age_focal %in% 30:35,
-    age_kin > 60
-  )
diff --git a/man/kin.Rd b/man/kin.Rd
index 83dbe04..5fbc92f 100644
--- a/man/kin.Rd
+++ b/man/kin.Rd
@@ -13,9 +13,9 @@ kin(
   output_cohort = NULL,
   output_period = NULL,
   output_kin = NULL,
+  output_age_focal = NULL,
   birth_female = 1/2.04,
-  stable = lifecycle::deprecated(),
-  U = lifecycle::deprecated()
+  summary_kin = TRUE
 )
 }
 \arguments{
@@ -36,12 +36,12 @@ in a more general perspective) with rows as ages (and columns as years in case o
 
 \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
 
-\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring,
-this needs to be set as 1.}
+\item{output_age_focal}{integer. Vector of ages to select (and make faster the run).}
 
-\item{stable}{logic. Deprecated. Use \code{time_invariant}.}
+\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring,}
 
-\item{U}{logic. Deprecated. Use \code{p}.}
+\item{summary_kin}{logical. Whether or not include \code{kin_summary} table (see output details). Default \code{TRUE}.
+this needs to be set as 1.}
 }
 \value{
 A list with:
diff --git a/man/kin2sex.Rd b/man/kin2sex.Rd
index be985aa..fde5cd9 100644
--- a/man/kin2sex.Rd
+++ b/man/kin2sex.Rd
@@ -16,9 +16,13 @@ kin2sex(
   pim = NULL,
   nf = NULL,
   nm = NULL,
+  Hf = NULL,
+  Hm = NULL,
   output_cohort = NULL,
   output_period = NULL,
-  output_kin = NULL
+  output_kin = NULL,
+  output_age_focal = NULL,
+  summary_kin = TRUE
 )
 }
 \arguments{
@@ -44,11 +48,19 @@ kin2sex(
 
 \item{nm}{numeric. Only for \code{time_invariant = FALSE}. Same as \code{pm} but for population distribution (counts or \verb{\%}). Optional.}
 
+\item{Hf}{numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for females with rows as causes and columns as ages, being the name of each col the age.}
+
+\item{Hm}{numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for males with rows as causes and columns as ages, being the name of each col the age.}
+
 \item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.}
 
 \item{output_period}{integer. Vector of period years for returning results. Should be within input data years range.}
 
 \item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
+
+\item{output_age_focal}{integer. Vector of ages to select (and make faster the run).}
+
+\item{summary_kin}{logical. Whether or not include \code{kin_summary} table (see output details). Default \code{TRUE}.}
 }
 \value{
 A list with:
diff --git a/man/kin_time_invariant_2sex_cod.Rd b/man/kin_time_invariant_2sex_cod.Rd
new file mode 100644
index 0000000..6645ca0
--- /dev/null
+++ b/man/kin_time_invariant_2sex_cod.Rd
@@ -0,0 +1,59 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_time_invariant_2sex_cod.R
+\name{kin_time_invariant_2sex_cod}
+\alias{kin_time_invariant_2sex_cod}
+\title{Estimate kin counts in a time invariant framework for two-sex model.}
+\usage{
+kin_time_invariant_2sex_cod(
+  pf = NULL,
+  pm = NULL,
+  ff = NULL,
+  fm = NULL,
+  Hf = NULL,
+  Hm = NULL,
+  sex_focal = "f",
+  birth_female = 1/2.04,
+  pif = NULL,
+  pim = NULL,
+  output_kin = NULL,
+  list_output = FALSE
+)
+}
+\arguments{
+\item{pf}{numeric. A vector of survival probabilities for females with same length as ages.}
+
+\item{pm}{numeric. A vector of survival probabilities for males with same length as ages.}
+
+\item{ff}{numeric. A vector of age-specific fertility rates for females with same length as ages.}
+
+\item{fm}{numeric. A vector of age-specific fertility rates for males with same length as ages.}
+
+\item{Hf}{numeric. A matrix with cause-specific hazards for females with rows as causes and columns as ages, being the name of each col the age.}
+
+\item{Hm}{numeric. A matrix with cause-specific hazards for males with rows as causes and columns as ages, being the name of each col the age.}
+
+\item{sex_focal}{character. "f" for female or "m" for male.}
+
+\item{birth_female}{numeric. Female portion at birth.}
+
+\item{pif}{numeric. For using some specific non-stable age distribution of childbearing for mothers (same length as ages). Default \code{NULL}.}
+
+\item{pim}{numeric. For using some specific non-stable age distribution of childbearing for fathers (same length as ages). Default \code{NULL}.}
+
+\item{output_kin}{character. kin to return, considering matrilineal names. For example "m" for parents, "d" for children, etc. See the \code{vignette} for all kin types.}
+
+\item{list_output}{logical. Results as a list with \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
+}
+\value{
+A data frame with focal´s age, related ages and type of kin
+(for example \code{d} is children, \code{oa} is older aunts/uncles, etc.), sex, alive and death. If \code{list_output = TRUE} then this is a list.
+}
+\description{
+Two-sex matrix framework for kin count and death estimates.This produces kin counts grouped by kin, age and sex of
+each relatives at each Focal´s age. For example, male cousins from aunts and uncles from different sibling's parents
+are grouped in one male count of cousins. This also produces kin deaths grouped by kin, age, sex of
+each relatives at each Focal´s age, and cause of death.
+}
+\details{
+See Caswell (2022) for details on formulas.
+}
diff --git a/man/kin_time_variant_2sex_cod.Rd b/man/kin_time_variant_2sex_cod.Rd
new file mode 100644
index 0000000..c0db9a8
--- /dev/null
+++ b/man/kin_time_variant_2sex_cod.Rd
@@ -0,0 +1,70 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_time_variant_2sex_cod.R
+\name{kin_time_variant_2sex_cod}
+\alias{kin_time_variant_2sex_cod}
+\title{Estimate kin counts in a time variant framework (dynamic rates) in a two-sex framework (Caswell, 2022)}
+\usage{
+kin_time_variant_2sex_cod(
+  pf = NULL,
+  pm = NULL,
+  ff = NULL,
+  fm = NULL,
+  Hf = NULL,
+  Hm = NULL,
+  sex_focal = "f",
+  birth_female = 1/2.04,
+  pif = NULL,
+  pim = NULL,
+  nf = NULL,
+  nm = NULL,
+  output_cohort = NULL,
+  output_period = NULL,
+  output_kin = NULL,
+  list_output = FALSE
+)
+}
+\arguments{
+\item{pf}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+
+\item{pm}{numeric. A vector (atomic) or  matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).}
+
+\item{ff}{numeric. Same as pf but for fertility rates.}
+
+\item{fm}{numeric. Same as pm but for fertility rates.}
+
+\item{Hf}{numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for females with rows as causes and columns as ages, being the name of each col the age.}
+
+\item{Hm}{numeric. A list where each list element (being the name of each list element the year) contains a matrix with cause-specific hazards for males with rows as causes and columns as ages, being the name of each col the age.}
+
+\item{sex_focal}{character. "f" for female or "m" for male.}
+
+\item{birth_female}{numeric. Female portion at birth. This multiplies \code{f} argument. If \code{f} is already for female offspring, this needs to be set as 1.}
+
+\item{pif}{numeric. For using some specific age distribution of childbearing for mothers (same length as ages). Default \code{NULL}.}
+
+\item{pim}{numeric. For using some specific age distribution of childbearing for fathers (same length as ages). Default \code{NULL}.}
+
+\item{nf}{numeric. Same as pf but for population distribution (counts or \verb{\%}). Optional.}
+
+\item{nm}{numeric. Same as pm but for population distribution (counts or \verb{\%}). Optional.}
+
+\item{output_cohort}{integer. Vector of year cohorts for returning results. Should be within input data years range.}
+
+\item{output_period}{integer. Vector of period years for returning results. Should be within input data years range.}
+
+\item{output_kin}{character. kin types to return: "m" for mother, "d" for daughter,...}
+
+\item{list_output}{logical. Results as a list with years elements (as a result of \code{output_cohort} and \code{output_period} combination), with a second list of \code{output_kin} elements, with focal´s age in columns and kin ages in rows (2 * ages, last chunk of ages for death experience). Default \code{FALSE}}
+}
+\value{
+A data.frame with year, cohort, Focal´s age, related ages, sex and type of kin (for example \code{d} is daughter, \code{oa} is older aunts, etc.), including living and dead kin at that age and sex.
+}
+\description{
+Two-sex matrix framework for kin count estimates with varying rates.
+This produces kin counts grouped by kin, age and sex of each relatives at each Focal´s age.
+For example, male cousins from aunts and uncles from different sibling's parents are grouped in one male count of cousins. This also produces kin deaths grouped by kin, age, sex of
+each relatives at each Focal´s age, and cause of death.
+}
+\details{
+See Caswell (2022) for details on formulas.
+}
diff --git a/man/timevarying_kin_2sex_cod.Rd b/man/timevarying_kin_2sex_cod.Rd
new file mode 100644
index 0000000..8bc0b87
--- /dev/null
+++ b/man/timevarying_kin_2sex_cod.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_time_variant_2sex_cod.R
+\name{timevarying_kin_2sex_cod}
+\alias{timevarying_kin_2sex_cod}
+\title{one time projection kin}
+\usage{
+timevarying_kin_2sex_cod(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
+}
+\arguments{
+\item{Ut}{numeric. A matrix of survival probabilities (or ratios).}
+
+\item{Ft}{numeric. A matrix of age-specific fertility rates.}
+
+\item{Ft_star}{numeric. Ft but for female fertility.}
+
+\item{pit}{numeric. A matrix with distribution of childbearing.}
+
+\item{sex_focal}{character. "f" for female or "m" for male.}
+
+\item{ages}{numeric.}
+
+\item{pkin}{numeric. A list with kin count distribution in previous year.}
+}
+\value{
+A list of 14 types of kin matrices (kin age by Focal age, blocked for two sex) projected one time interval.
+}
+\description{
+one time projection kin. internal function.
+}
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index 8ee0d94..97f4b4a 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -11,7 +11,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, eval = F, include=FALSE}
+```{r, eval = T, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 library(devtools); load_all()
 ```
@@ -275,4 +275,43 @@ bind_rows(
   facet_grid(col = vars(kin), row = vars(age_focal), scales = "free")
 ```
 
+### 2. Causes of death
+
+Now assume we have two causes of death, where the risk of the first one is half the other for females but 2/3 for males, in both cases for ages greater than 50. We need to set tow matrix with dimension 2 by 101 (number of causes by number of ages).
+
+```{r}
+Hf <- matrix(c( .5, 1), nrow = 2, ncol = length(fra_surv_f))
+Hm <- matrix(c(.33, 1), nrow = 2, ncol = length(fra_surv_f))
+Hf[,1:50] <- Hm[,1:50] <- 1
+```
+
+This is a generalization of Caswell [-@Caswell2023] approach. Originally the inputs in matrix $H$ are the hazard rates, but works like a relative risk factors realted to undelying death probability (see section 2.3 and formula 30 in section A.1). Now we run the time-invariant two-sex model by cause of death for France 2012, assuming our death count distribution based in the two competing causes:
+
+```{r}
+kin_out_cod_invariant <- kin2sex(
+                      pf = fra_surv_f,
+                      pm = fra_surv_m, 
+                      ff = fra_fert_f, 
+                      fm = fra_fert_m,
+                      Hf = Hf,
+                      Hm = Hm,
+                      time_invariant = TRUE)
+```
+
+The output in this case is always the `kin_full` type. Let´s see a plot with the death distribution by age and cause of Focal´s parents when Focal is 30 yo.
+
+```{r}
+kin_out_cod_invariant %>% 
+  filter(kin == "m", age_focal == 30) %>% 
+  summarise(deadcause1 = sum(deadcause1),
+            deadcause2 = sum(deadcause2), .by = c(age_kin, sex_kin)) %>%
+  pivot_longer(deadcause1:deadcause2) %>% 
+  ggplot(aes(age_kin, value, col = sex_kin, linetype = name)) +
+  geom_line() + 
+  labs(y = "Parent's death count") +
+  theme_bw()
+```
+
+Because all the death experience of Focal is for apretns older than 50, is clear the realtive difference between the causes for each sex. The sum of the counts by sex gives the same result than total deaths by sex at that age in the general case (section 2, without splitting death probabilities by cause). The number of possible causes to add is not limited, but consider that it get more time-consuming. If we are dealing with a time-variant case, then a list by sex ($Hf$ and $Hm$) must be provided, with $H$ matrix for each year as elements, in the same order than mortality and fertility components.
+
 ## References
diff --git a/vignettes/references.bib b/vignettes/references.bib
index 19e08e3..a2fc0ff 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -1,3 +1,15 @@
+@article{Caswell2023,
+	author = {Caswell, Hal and Margolis, Rachel and Verdery, Ashton},
+	title = {{The formal demography of kinship V: Kin loss, bereavement, and causes of death}},
+	journal = {Demographic Research},
+	volume = {49},
+	pages = {1163--1200},
+	year = {2023},
+	month = dec,
+	issn = {1435-9871},
+	publisher = {Demographic Research},
+	url = {https://www.demographic-research.org/articles/volume/49/41}
+}
 
 @article{caswell_formal_2019,
 	title = {The formal demography of kinship: {A} matrix formulation},

From 85830e46c8b05cb21f083a8cf71fab76a8dafcdc Mon Sep 17 00:00:00 2001
From: Alburez-Gutierrez <alburezgutierrez@demogr.mpg.de>
Date: Tue, 17 Sep 2024 09:23:37 +0200
Subject: [PATCH 36/89] updated vignette COD

---
 vignettes/Reference_TwoSex.Rmd | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index 97f4b4a..12289d9 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -277,7 +277,7 @@ bind_rows(
 
 ### 2. Causes of death
 
-Now assume we have two causes of death, where the risk of the first one is half the other for females but 2/3 for males, in both cases for ages greater than 50. We need to set tow matrix with dimension 2 by 101 (number of causes by number of ages).
+Now assume we have two causes of death (COD). For females, the risk of the first COD is half the risk of the second COD for ages greater than 50. For males, the risk of the first COD is 2/3 of the second COD for ages greater than 50. We operationalize this using two matrices with dimension 2 by 101 (number of causes by number of ages).
 
 ```{r}
 Hf <- matrix(c( .5, 1), nrow = 2, ncol = length(fra_surv_f))
@@ -285,7 +285,7 @@ Hm <- matrix(c(.33, 1), nrow = 2, ncol = length(fra_surv_f))
 Hf[,1:50] <- Hm[,1:50] <- 1
 ```
 
-This is a generalization of Caswell [-@Caswell2023] approach. Originally the inputs in matrix $H$ are the hazard rates, but works like a relative risk factors realted to undelying death probability (see section 2.3 and formula 30 in section A.1). Now we run the time-invariant two-sex model by cause of death for France 2012, assuming our death count distribution based in the two competing causes:
+This is a generalization of the approach outlined by Caswell [-@Caswell2023]. In the original formulation, the inputs in matrix $H$ are the hazard rates. Here, we treat them like a relative risk factor related to the underlying probability of dying. For more details, see section 2.3 and formula 30 in section A.1 of Caswell [-@Caswell2023]. Now we run the time-invariant two-sex model by COD for France 2012, assuming a death count distribution based on the two competing causes; note that the `kin2sex` function now takes the arguments `Hf` and `Hm` but the other arguments remain unchanged:
 
 ```{r}
 kin_out_cod_invariant <- kin2sex(
@@ -298,7 +298,13 @@ kin_out_cod_invariant <- kin2sex(
                       time_invariant = TRUE)
 ```
 
-The output in this case is always the `kin_full` type. Let´s see a plot with the death distribution by age and cause of Focal´s parents when Focal is 30 yo.
+The output of `kin2sex` is the the `kin_full` data frame that we have encountered before. The only differences is that `kin_full` now includes one column for each COD specified in the input. Therefore, the number of columns will vary depending on how many COD you are considering!
+
+```{r}
+head(kin_out_cod_invariant)
+```
+
+We can now plot the death distribution by age and COD of Focal's parents when Focal is 30 yo.
 
 ```{r}
 kin_out_cod_invariant %>% 
@@ -308,10 +314,12 @@ kin_out_cod_invariant %>%
   pivot_longer(deadcause1:deadcause2) %>% 
   ggplot(aes(age_kin, value, col = sex_kin, linetype = name)) +
   geom_line() + 
-  labs(y = "Parent's death count") +
+  labs(y = "Expected number of parental deaths") +
   theme_bw()
 ```
 
-Because all the death experience of Focal is for apretns older than 50, is clear the realtive difference between the causes for each sex. The sum of the counts by sex gives the same result than total deaths by sex at that age in the general case (section 2, without splitting death probabilities by cause). The number of possible causes to add is not limited, but consider that it get more time-consuming. If we are dealing with a time-variant case, then a list by sex ($Hf$ and $Hm$) must be provided, with $H$ matrix for each year as elements, in the same order than mortality and fertility components.
+In this simplified example, the parents of Focal only died after age 50. This helped highlight the relative difference between the COD for each sex. Note that the sum of the death counts by sex gives the same result as the total deaths by sex at that age in the less complex model (i.e., the one that does not consider COD, see section 2 of this guide). 
+
+You can add as many COD as you want, but keep in mind that this can be computationally intensive. For time-variant kinship models that consider COD, you must provide a list of matrices by sex ($Hf$ and $Hm$). The elements of this list should be $H$ matrices for each year (following the same order than the mortality and fertility components). 
 
 ## References

From 578456508a01cd3f5f2b516c7ff06d96b7c894f9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Iv=C3=A1n=20Williams?= <ivanwilliams1985@gmail.com>
Date: Tue, 24 Sep 2024 11:13:22 -0300
Subject: [PATCH 37/89] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2bb3c6b..9255f10 100644
--- a/README.md
+++ b/README.md
@@ -116,7 +116,7 @@ does not load, you may need to install the package as
 
 ## Citation
 
-Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell.
+Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team.
 (2021) DemoKin: An R package to implement demographic matrix kinship
 models. URL: <https://github.com/IvanWilli/DemoKin>.
 

From ae5e0dc755548d3b2085a246dc9c60be09ac6afa Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 15:49:42 +0100
Subject: [PATCH 38/89] commiting files to my fork

---
 R/kin_multi_stage_time_variant_2sex.R         | 1060 +++++++++++++++++
 data/Female_parity_fert_list_UK.Rds           |  Bin 0 -> 62341 bytes
 data/Female_parity_mortality_list_UK.Rds      |  Bin 0 -> 37381 bytes
 data/Male_parity_fert_list_UK.Rds             |  Bin 0 -> 62341 bytes
 data/Male_parity_mortality_list_UK.Rds        |  Bin 0 -> 45416 bytes
 data/Parity_transfers_by_age_UK.Rds           |  Bin 0 -> 136152 bytes
 data/Redistribution_by_parity_list_UK.Rds     |  Bin 0 -> 321 bytes
 man/all_kin_dy.Rd                             |   48 +
 man/all_kin_dy_TV.Rd                          |   94 ++
 man/create_cumsum_df.Rd                       |   41 +
 man/create_full_dists_df.Rd                   |   41 +
 man/kin_multi_stage_time_variant_2sex.Rd      |   63 +
 man/pi_mix.Rd                                 |   33 +
 man/pi_mix_TV.Rd                              |   31 +
 man/pi_mix_TV_parity.Rd                       |   35 +
 man/pi_mix_parity.Rd                          |   33 +
 .../test-kin_twosex_multistate_timevariant.R  |   77 ++
 ...eference_TwoSex_MultiState_TimeVariant.Rmd |  255 ++++
 18 files changed, 1811 insertions(+)
 create mode 100644 R/kin_multi_stage_time_variant_2sex.R
 create mode 100644 data/Female_parity_fert_list_UK.Rds
 create mode 100644 data/Female_parity_mortality_list_UK.Rds
 create mode 100644 data/Male_parity_fert_list_UK.Rds
 create mode 100644 data/Male_parity_mortality_list_UK.Rds
 create mode 100644 data/Parity_transfers_by_age_UK.Rds
 create mode 100644 data/Redistribution_by_parity_list_UK.Rds
 create mode 100644 man/all_kin_dy.Rd
 create mode 100644 man/all_kin_dy_TV.Rd
 create mode 100644 man/create_cumsum_df.Rd
 create mode 100644 man/create_full_dists_df.Rd
 create mode 100644 man/kin_multi_stage_time_variant_2sex.Rd
 create mode 100644 man/pi_mix.Rd
 create mode 100644 man/pi_mix_TV.Rd
 create mode 100644 man/pi_mix_TV_parity.Rd
 create mode 100644 man/pi_mix_parity.Rd
 create mode 100644 tests/testthat/test-kin_twosex_multistate_timevariant.R
 create mode 100644 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
new file mode 100644
index 0000000..c930147
--- /dev/null
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -0,0 +1,1060 @@
+
+
+#' Estimate kin counts by age, stage, and sex, in a time variant framework
+
+#' @description Implementation of combined formal demographic models: Caswell II,III,IV.
+
+#' @param U_list_females list with matrix entries: period-specific female survival probabilities. Age in rows and states in columns.
+#' @param U_list_males list with matrix entries: period-specific male survival probabilities. Age in rows and states in columns.
+#' @param F_list_females list with matrix with elements: period-specific female fertility (age in rows and states in columns).
+#' @param F_list_males list with matrix entries: period-specific male fertility (age in rows and states in columns).
+#' @param T_list_females list of lists with matrix entries: each outer list entry is period-specific, and composed of
+#'                     a list of stochastic matrices which describe age-specific female probabilities of transferring stage
+#' @param T_list_males list of lists with matrix entries: each outer list entry is period-specific, and composed of
+#'                     a list of stochastic matrices which describe age-specific male probabilities of transferring stage
+#' @param H_list list with matrix entries: redistribution of newborns across each stage to a specific age-class
+#' @param birth_female numeric. ratio of males to females in population
+#' @param parity logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default `TRUE`.
+#' @param output_kin vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.
+#' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if FALSE, and kin by their age*stage distribution by age of Focal if TRUE.
+#' @param sex_Focal character. Female or Male as the user requests
+#' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
+#' @param n_inc numeric. The age/time-increment used in the discretisation of the continuum.
+#' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
+#'
+#' @return A data frame with focal´s age, related ages, stages, sexes, and types of kin for each time-period
+
+#' @export
+#'
+kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
+                                              U_list_males = NULL,
+                                              F_list_females = NULL,
+                                              F_list_males = NULL,
+                                              T_list_females = NULL,
+                                              T_list_males = NULL,
+                                              H_list = NULL,
+                                              birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
+                                              parity = FALSE,
+                                              output_kin = FALSE,
+                                              summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
+                                              sex_Focal = "Female",
+                                              initial_stage_Focal = NULL,
+                                              n_inc = NULL, ## n_inc is the age-class, time-class increment (e.g., 1year,5year,10year)
+                                              output_years){
+
+  no_years <- length(U_list_females)
+  na <- nrow(U_list_females[[1]])
+  ns <- ncol(U_list_females[[1]])
+
+  # Ensure inputs are lists of matrices and that the timescale same length
+  if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
+  if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+  if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+  if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+
+  ### Define empty lists for the accumulated kin of Focals's life-course -- each list entry will reflect a time-period
+  changing_pop_struct <- list()
+  Focal_array <- list()
+  mom_array <- list()
+  gran_array <- list()
+  great_gran_array <- list()
+  daughter_array <- list()
+  younger_sis_array <- list()
+  grand_daughter_array <-list()
+  great_grand_daughter_array <- list()
+  older_sister_array <- list()
+  younger_aunt_array <- list()
+  older_aunt_array <- list()
+  younger_niece_array <- list()
+  older_niece_array <- list()
+  younger_cousin_array <- list()
+  older_cousin_array <- list()
+
+  ### At each time-period we: 1) -- construct the time-variant projection matrices:
+  ###                               U_tilde : transfers across stage and advances age
+  ###                               F_tilde : makes newborns from stage/age; puts them to stage/age
+  ###                         2) -- project Focal and kin using above projection matrices
+
+  pb <- progress::progress_bar$new(
+    format = "Timescale [:bar] :percent",
+    total = no_years + 1, clear = FALSE, width = 60)
+  tictoc::tic()
+  for(year in 1:no_years){
+    pb$tick()
+    T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
+    T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
+    T_f_list <- list()
+    T_m_list <- list()
+    F_f_list <- list()
+    F_m_list <- list()
+    U_f_list <- list()
+    U_m_list <- list()
+    H_list2 <- list()
+
+    for(stage in 1:ns){
+      Uf <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      Matrix::diag(Uf[-1,-ncol(Uf)]) <- U_list_females[[year]][1:(na-1),stage]
+      Uf[na,na] <- U_list_females[[year]][na,stage]
+      Um <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      Matrix::diag(Um[-1,-ncol(Um)]) <- U_list_males[[year]][1:(na-1),stage]
+      Um[na,na] <- U_list_males[[year]][na,stage]
+      U_f_list[[(1+length(U_f_list))]] <- Uf
+      U_m_list[[(1+length(U_m_list))]] <- Um
+      H_mat <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      H_mat[1,] <- 1
+      H_list2[[(1+length(H_list2))]] <- H_mat
+    }
+    for(age in 1:na){
+      T_f <- T_data_f[[age]]
+      T_m <- T_data_m[[age]]
+      T_f_list[[(1+length(T_f_list))]] <- T_f
+      T_m_list[[(1+length(T_m_list))]] <- T_m
+      F_f <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
+      F_m <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
+      F_f[1,] <- F_list_females[[year]][age,]
+      F_m[1,] <- F_list_males[[year]][age,]
+      F_f_list[[(1+length(F_f_list))]] <- F_f
+      F_m_list[[(1+length(F_m_list))]] <- F_m
+    }
+    ## create the appropriate block-diagonal matrices
+    U_f_BDD <- block_diag_function(U_f_list) ## direct sum of female survivorship, independent over stage (ns diagonal blocks)
+    U_m_BDD <- block_diag_function(U_m_list) ## direct sum of male survivorship, independent over stage (ns diagonal blocks)
+    H_BDD <- block_diag_function(H_list2) ## direct sum of which age newborns enter, independent over stage (ns diagonal blocks)
+    T_f_BDD <- block_diag_function(T_f_list) ## direct sum of female stage transitions, independent over age (na diagonal blocks)
+    T_m_BDD <- block_diag_function(T_m_list) ## direct sum of male stage transitions, independent over age (na diagonal blocks)
+    F_f_BDD <- block_diag_function(F_f_list) ## direct sum of female stage->stage reproductions, independent over age (na blocks)
+    F_m_BDD <- block_diag_function(F_m_list) ## direct sum of male stage->stage reproductions, independent over age (na blocks)
+
+    ## create the appropriate projection matrices
+    U_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
+      U_f_BDD %*%
+      K_perm_mat(ns, na) %*%
+      T_f_BDD
+
+    ## create sex-specific age*stage projections
+    U_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
+      U_m_BDD %*%
+      K_perm_mat(ns, na) %*%
+      T_m_BDD
+
+    F_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
+      H_BDD %*%
+      K_perm_mat(ns, na) %*%
+      F_f_BDD
+
+    F_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
+      H_BDD %*%
+      K_perm_mat(ns, na) %*%
+      F_m_BDD
+
+    ## if year == 1 we are at the boundary condition t=0 apply time-invariant kinship projections
+    if(year == 1){
+      ## Output of the static model
+      kin_out_1 <- all_kin_dy(U_tilde_females,
+                              U_tilde_males ,
+                              F_tilde_females,
+                              F_tilde_males,
+                              1-birth_female,
+                              na,
+                              ns,
+                              parity,
+                              sex_Focal,
+                              initial_stage_Focal)
+      ### Relative lists' first entries
+      Focal_array[[(1+length(Focal_array))]] <- kin_out_1[["Focal"]]
+      daughter_array[[(1+length(daughter_array))]] <- kin_out_1[["d"]]
+      grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out_1[["gd"]]
+      great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out_1[["ggd"]]
+      mom_array[[(1+length(mom_array))]] <- kin_out_1[["m"]]
+      gran_array[[(1+length(gran_array))]] <- kin_out_1[["gm"]]
+      great_gran_array[[(1+length(great_gran_array))]] <- kin_out_1[["ggm"]]
+      younger_sis_array[[( 1+length(younger_sis_array))]] <- kin_out_1[["ys"]]
+      older_sister_array[[(1+length(older_sister_array))]] <- kin_out_1[["os"]]
+      younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out_1[["ya"]]
+      older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out_1[["oa"]]
+      younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out_1[["nys"]]
+      older_niece_array[[(1+length(older_niece_array))]] <- kin_out_1[["nos"]]
+      younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out_1[["cya"]]
+      older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out_1[["coa"]]
+      changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out_1[["ps"]]
+
+    }
+    updating_Focal <- Focal_array[[year]]
+    updating_daughter <- daughter_array[[year]]
+    updating_grand_daughter <- grand_daughter_array[[year]]
+    updating_great_grand_daughter <- great_grand_daughter_array[[year]]
+    updating_mom <- mom_array[[year]]
+    updating_gran <- gran_array[[year]]
+    updating_great_gran <- great_gran_array[[year]]
+    updating_younger_sis <- younger_sis_array[[year]]
+    updating_older_sis <- older_sister_array[[year]]
+    updating_youner_aunt <- younger_aunt_array[[year]]
+    updating_older_aunt <- older_aunt_array[[year]]
+    updating_younger_niece <- younger_niece_array[[year]]
+    updating_older_niece <- older_niece_array[[year]]
+    updating_younger_cousin <- younger_cousin_array[[year]]
+    updating_older_cousin <- older_cousin_array[[year]]
+    updating_pop_struct <- changing_pop_struct[[year]]
+
+    ## Output of the time-variant model
+    kin_out <- all_kin_dy_TV(U_tilde_females,
+                             U_tilde_males,
+                             F_tilde_females,
+                             F_tilde_males,
+                             1-birth_female,
+                             na,
+                             ns,
+                             parity,
+                             sex_Focal,
+                             initial_stage_Focal,
+                             updating_Focal,
+                             updating_daughter,
+                             updating_grand_daughter,
+                             updating_great_grand_daughter,
+                             updating_mom,
+                             updating_gran,
+                             updating_great_gran,
+                             updating_older_sis,
+                             updating_younger_sis,
+                             updating_older_niece,
+                             updating_younger_niece,
+                             updating_older_aunt,
+                             updating_youner_aunt,
+                             updating_older_cousin,
+                             updating_younger_cousin,
+                             updating_pop_struct)
+    ## Relative lists entries correspond to timescale periods (each entry an kin age*stage*2 by Focal age matrix)
+    Focal_array[[(1+length(Focal_array))]] <- kin_out[["Focal"]]
+    daughter_array[[(1+length(daughter_array))]] <- kin_out[["d"]]
+    grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out[["gd"]]
+    great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out[["ggd"]]
+    mom_array[[(1+length(mom_array))]] <- kin_out[["m"]]
+    gran_array[[(1+length(gran_array))]] <- kin_out[["gm"]]
+    great_gran_array[[(1+length(great_gran_array))]] <- kin_out[["ggm"]]
+    younger_sis_array[[(1+length(younger_sis_array))]] <- kin_out[["ys"]]
+    older_sister_array[[(1+length(older_sister_array))]] <- kin_out[["os"]]
+    younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out[["ya"]]
+    older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out[["oa"]]
+    younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out[["nys"]]
+    older_niece_array[[(1+length(older_niece_array))]] <- kin_out[["nos"]]
+    younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out[["cya"]]
+    older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out[["coa"]]
+    changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out[["ps"]]
+  }
+  tictoc::toc()
+  ## create a list of output kin -- each element a time-period specific list of matrices
+  ## label the kin names to match DemoKin:
+  relative_data <- list("Focal" = Focal_array,
+                        "d" = daughter_array,
+                        "gd" = grand_daughter_array,
+                        "ggd" = great_grand_daughter_array,
+                        "m" = mom_array,
+                        "gm" = gran_array,
+                        "ggm" = great_gran_array,
+                        "ys" = younger_sis_array,
+                        "os" = older_sister_array,
+                        "ya" = younger_aunt_array,
+                        "oa" = older_aunt_array,
+                        "nys" = younger_niece_array,
+                        "nos" = older_niece_array,
+                        "cya" = younger_cousin_array,
+                        "coa" = older_cousin_array)
+
+  relative_names <- names(relative_data)
+  ## create a nice data frame output
+  if(summary_kin){
+    kin_out <- create_cumsum_df(relative_data,
+                                relative_names,
+                                output_years[1]:output_years[length(output_years)],
+                                output_years[1],
+                                na,
+                                ns,
+                                n_inc,
+                                output_kin)}
+  else{
+    kin_out <- create_full_dists_df(relative_data,
+                                    relative_names,
+                                    output_years[1]:output_years[length(output_years)],
+                                    output_years[1],
+                                    na,
+                                    ns,
+                                    n_inc,
+                                    output_kin)}
+
+  return(kin_out)
+}
+
+
+#' Title time invariant two-sex multi-state kin projections
+#'
+#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
+#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
+#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
+#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
+#' @param alpha scalar. birth ratio (male:female)
+#' @param na scalar. number of ages.
+#' @param ns scalar. number of stages.
+#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
+#' @param sex_Focal logical. Female or Male
+#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
+#'
+#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+#' yielding the age*stage distribution of kin for each age of Focal
+#'
+#'
+#'
+all_kin_dy <- function(Uf,
+                       Um,
+                       Ff,
+                       Fm,
+                       alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
+                       na, ## na = number of ages
+                       ns, ## ns = number of stages
+                       Parity,
+                       sex_Focal, ## binary "F" or "M"
+                       Initial_stage_Focal){
+
+  n <- nrow(Uf) ## number of ages * stages for each sex
+
+  ## Projection matrices:
+
+  ## Uproj is a block diagonal matrix of block-structured Age*Stage matrices; independently over sex transfers individuals across stage and up age
+  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
+  ## Fproj is a Sex-block-structured matrix of block-structured Age*Stage matrices where males and females BOTH reproduce (by stage)
+  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  Fproj[1:n, 1:n] <- (1-alpha)*Ff ## Ff is Age*Stage block structured giving rate at which females in age-stage produce individuals in age-stage
+  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
+  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm ## Fm is Age*Stage block structured giving rate at which males in age-stage produce individuals in age-stage
+  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
+
+  ## Fprojstar is a Sex-block-structured matrix of block-structured Age*Stage matrices where ONLY females reproduce
+  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
+  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
+  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
+
+  ## The stable population structure is an age*stage*sex vector:
+  ##                                                            1:n gives the female age*stage structure
+  ##                                                            (1+n):2n gives the male age*stage structure
+  population_age_stage_structure <- SD(Uproj + Fprojstar)
+
+  ### Stable distribution of mothers needs adjusting if we work with parity
+  if(Parity){
+    Initial_stage_Focal <- 1
+
+    population_age_stage_of_parenting <- pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+  else{
+    population_age_stage_of_parenting <- pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+
+  ####################################### The dynamics of Kinship, starting with Focal who is no longer a unit vector
+
+  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
+  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
+  m_t <- get_G(Um, na, ns)
+  G_tilde <- block_diag_function(list(f_t,m_t))
+  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  IC_Focal <- rep(0, 2*n)
+  if(sex_Focal == "Female"){
+    entry <- 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1}
+  else{
+    entry <- n + 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1
+  }
+
+  ### empty kin matrices for all of Focal's kin
+  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_aunt_uncle  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+
+
+  ### Initial distributions for kin with non-zero deterministic initial conditions:
+  # Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
+  X_Focal[,1] <- IC_Focal
+  X_parents[, 1] <- mothers_age_stage
+
+  ### projection all kin with deterministic initial conditions
+  for(i in 1 : (na-1)){
+    X_Focal[,i+1] <- G_tilde %*% X_Focal[,i]
+    X_parents[, i+1] <- Uproj %*% X_parents[, i]
+    X_younger_sibs[,i+1] <- Uproj %*% X_younger_sibs[,i] + Fprojstar %*% X_parents[,i]
+    X_younger_niece_nephew[,i+1] <- Uproj %*% X_younger_niece_nephew[,i] + Fproj %*% X_younger_sibs[,i]
+    X_children[,i+1] <- Uproj %*% X_children[,i] + Fproj %*% X_Focal[,i]
+    X_grand_children[,i+1] <- Uproj %*% X_grand_children[,i] + Fproj %*% X_children[,i]
+    X_great_grand_children[,i+1] <- Uproj %*% X_great_grand_children[,i] + Fproj %*% X_grand_children[,i]
+  }
+
+  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
+  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
+  IC_f_grand_pars <- mothers_age_dist
+  IC_m_grand_pars <- fathers_age_dist
+  IC_f_great_grand_pars <- mothers_age_dist
+  IC_m_great_grand_pars <- fathers_age_dist
+  IC_older_sibs_f <- mothers_age_dist
+  IC_younger_aunts_uncles_f <- mothers_age_dist
+  IC_younger_aunts_uncles_m <- fathers_age_dist
+  IC_older_niece_nephew_f <- mothers_age_dist
+  for(ic in 1 : (na)){
+    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*X_parents[,ic] ## IC the sum of parents of Focal's parents,
+    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*X_grand_parents[,ic]
+    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*X_children[,ic]
+    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*X_grand_children[,ic]
+    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*X_younger_sibs[,ic]
+  }
+
+  ### Projections of grand parenst, older sibs, younger aunts/uncles, older nieces/nephews
+  for(i in 1: (na-1)){
+    X_grand_parents[, i+1] <- Uproj %*% X_grand_parents[, i]
+    X_great_grand_parents[, i+1] <- Uproj %*% X_great_grand_parents[, i]
+    X_older_sibs[,i+1] <- Uproj %*% X_older_sibs[,i]
+    X_older_niece_nephew[,i+1] <- Uproj %*% X_older_niece_nephew[,i] + Fproj %*% X_older_sibs[,i]
+    X_younger_aunts_uncles[,i+1] <- Uproj %*% X_younger_aunts_uncles[,i] + Fprojstar %*% X_grand_parents[,i]
+  }
+
+  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
+  ## older unts/uncles, older cousins, younger cousins
+  IC_older_aunt_uncle_f <- mothers_age_dist
+  IC_older_aunt_uncle_m <- fathers_age_dist
+  IC_older_cousins_f <- mothers_age_dist
+  IC_older_cousins_m <- fathers_age_dist
+  IC_younger_cousins_f <- mothers_age_dist
+  IC_younger_cousins_m <- fathers_age_dist
+  for(ic in 1 : (na-1)){
+    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*X_older_sibs[,ic]
+    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*X_older_niece_nephew[,ic]
+    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*X_younger_niece_nephew[,ic]
+  }
+
+  ## Projections of older unts/uncles, older cousins, younger cousins
+  for(i in 1: (na-1)){
+    X_older_aunt_uncle[,i+1] <- Uproj %*% X_older_aunt_uncle[,i]
+    X_older_cousins[,i+1] <- Uproj %*% X_older_cousins[,i] + Fproj %*% X_older_aunt_uncle[,i]
+    X_younger_cousins[,i+1] <- Uproj %*% X_younger_cousins[,i] + Fproj %*% X_younger_aunts_uncles[,i]
+  }
+
+  #### OUTPUT of all kin
+  return(list("Focal" = X_Focal,
+              "d" = X_children,
+              "gd" = X_grand_children,
+              "ggd" = X_great_grand_children,
+              "m" = X_parents,
+              "gm" = X_grand_parents,
+              "ggm" = X_great_grand_parents,
+              "os" = X_older_sibs,
+              "ys" = X_younger_sibs,
+              "nos" = X_older_niece_nephew,
+              "nys" = X_younger_niece_nephew,
+              "oa" = X_older_aunt_uncle,
+              "ya" = X_younger_aunts_uncles,
+              "coa" = X_older_cousins,
+              "cya" = X_younger_cousins,
+              "ps" = population_age_stage_structure
+  ))
+}
+
+
+#' Title time-variant two-sex multi-state kin projections
+#'
+#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
+#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
+#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
+#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
+#' @param alpha scalar. birth ratio (male:female)
+#' @param na scalar. number of ages.
+#' @param ns scalar. number of stages.
+#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
+#' @param sex_Focal logical. Female or Male
+#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
+#' @param previous_kin_Focal matrix. last years kinship output.
+#' @param prev_kin_children matrix. last years kinship output.
+#' @param prev_kin_grandchildren matrix. last years kinship output.
+#' @param prev_kin_greatgrandchildren matrix. last years kinship output.
+#' @param prev_kin_parents matrix. last years kinship output.
+#' @param prev_kin_grand_parents matrix. last years kinship output.
+#' @param prev_kin_older_sibs matrix. last years kinship output.
+#' @param prev_kin_younger_sibs matrix. last years kinship output.
+#' @param prev_kin_older_niece_nephew matrix. last years kinship output.
+#' @param prev_kin_younger_niece_nephew matrix. last years kinship output.
+#' @param prev_kin_older_aunts_uncles matrix. last years kinship output.
+#' @param prev_kin_younger_aunts_uncles matrix. last years kinship output.
+#' @param prev_kin_older_cousins matrix. last years kinship output.
+#' @param prev_kin_younger_cousins matrix. last years kinship output.
+#' @param previous_population_age_stage_structure vector. The transient "population structure" (age*stage distributed)
+#'
+#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+#' yielding the age*stage distribution of kin for each age of Focal
+#'
+#'
+all_kin_dy_TV <- function(Uf,
+                          Um,
+                          Ff,
+                          Fm,
+                          alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
+                          na, ## number of ages
+                          ns, ## number of stages
+                          Parity,
+                          sex_Focal,
+                          Initial_stage_Focal,
+                          previous_kin_Focal,
+                          prev_kin_children,
+                          prev_kin_grandchildren,
+                          prev_kin_greatgrandchildren,
+                          prev_kin_parents,
+                          prev_kin_grand_parents,
+                          prev_kin_great_grand_parents,
+                          prev_kin_older_sibs,
+                          prev_kin_younger_sibs,
+                          prev_kin_older_niece_nephew,
+                          prev_kin_younger_niece_nephew,
+                          prev_kin_older_aunts_uncles,
+                          prev_kin_younger_aunts_uncles,
+                          prev_kin_older_cousins,
+                          prev_kin_younger_cousins,
+                          previous_population_age_stage_structure){
+
+  n <- nrow(Uf)
+  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
+  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  Fproj[1:n, 1:n] <- (1-alpha)*Ff
+  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
+  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm
+  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
+  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
+  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
+  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
+
+  population_age_stage_structure <- previous_population_age_stage_structure
+  population_age_stage_structure <- population_age_stage_structure/sum(population_age_stage_structure)
+  population_age_stage_structure_next <- (Uproj + Fprojstar)%*%population_age_stage_structure
+
+  ### Stable distribution of mothers needs adjusting if we work with parity
+  if(Parity){
+    Initial_stage_Focal <- 1
+
+    population_age_stage_of_parenting <- pi_mix_TV_parity(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+  else{
+
+    population_age_stage_of_parenting <- pi_mix_TV(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+
+  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
+  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
+  m_t <- get_G(Um, na, ns)
+  G_tilde <- block_diag_function(list(f_t,m_t))
+  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  IC_Focal <- rep(0, 2*n)
+  if(sex_Focal == "Female"){
+    entry <- 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1}
+  else{
+    entry <- n + 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1
+  }
+
+  ### empty kin matrices for all of Focal's kin
+  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_aunt_uncle <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+
+  ### Initial distributions for kin with non-zero deterministic initial conditions:
+  ## Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
+  X_Focal[,1] <- IC_Focal
+  X_parents[, 1] <- mothers_age_stage
+  ### projection all above kin with deterministic initial conditions
+  for(i in 1 : (na-1)){
+    X_Focal[,i+1] <- G_tilde %*% previous_kin_Focal[,i]
+    X_parents[, i+1] <- Uproj %*% prev_kin_parents[, i]
+    X_younger_sibs[,i+1] <- Uproj %*% prev_kin_younger_sibs[,i] + Fprojstar %*% prev_kin_parents[,i]
+    X_younger_niece_nephew[,i+1] <- Uproj %*% prev_kin_younger_niece_nephew[,i] + Fproj %*% prev_kin_younger_sibs[,i]
+    X_children[,i+1] <- Uproj %*% prev_kin_children[,i] + Fproj %*% previous_kin_Focal[,i]
+    X_grand_children[,i+1] <- Uproj %*% prev_kin_grandchildren[,i] + Fproj %*% prev_kin_children[,i]
+    X_great_grand_children[,i+1] <- Uproj %*% prev_kin_greatgrandchildren[,i] + Fproj %*% prev_kin_grandchildren[,i]
+  }
+
+  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
+  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
+  IC_f_grand_pars <- mothers_age_dist
+  IC_m_grand_pars <- fathers_age_dist
+  IC_f_great_grand_pars <- mothers_age_dist
+  IC_m_great_grand_pars <- fathers_age_dist
+  IC_younger_aunts_uncles_f <- mothers_age_dist
+  IC_younger_aunts_uncles_m <- fathers_age_dist
+  IC_older_sibs_f <- mothers_age_dist
+  IC_older_niece_nephew_f <- mothers_age_dist
+  for(ic in 1 : (na)){
+    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*prev_kin_parents[,ic] ## IC the sum of parents of Focal's parents,
+    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*prev_kin_grand_parents[,ic]
+    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*prev_kin_children[,ic]
+    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*prev_kin_grandchildren[,ic]
+    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*prev_kin_younger_sibs[,ic]
+  }
+
+  ### Projections of older sibs, younger aunts/uncles, older nieces/nephews
+  for(i in 1: (na-1)){
+    X_grand_parents[, i+1] <- Uproj %*% prev_kin_grand_parents[, i]
+    X_great_grand_parents[, i+1] <- Uproj %*% prev_kin_great_grand_parents[, i]
+    X_older_sibs[,i+1] <- Uproj %*% prev_kin_older_sibs[,i]
+    X_older_niece_nephew[,i+1] <- Uproj %*% prev_kin_older_niece_nephew[,i] + Fproj %*% prev_kin_older_sibs[,i]
+    X_younger_aunts_uncles[,i+1] <- Uproj %*% prev_kin_younger_aunts_uncles[,i] + Fprojstar %*% prev_kin_grand_parents[,i]
+  }
+
+  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
+  ## older unts/uncles, older cousins, younger cousins
+  IC_older_aunt_uncle_f <- mothers_age_dist
+  IC_older_aunt_uncle_m <- fathers_age_dist
+  IC_older_cousins_f <- mothers_age_dist
+  IC_older_cousins_m <- fathers_age_dist
+  IC_younger_cousins_f <- mothers_age_dist
+  IC_younger_cousins_m <- fathers_age_dist
+  for(ic in 1 : (na-1)){
+    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*prev_kin_older_sibs[,ic]
+    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*prev_kin_older_niece_nephew[,ic]
+    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*prev_kin_younger_niece_nephew[,ic]
+  }
+
+  ## Projections of older unts/uncles, older cousins, younger cousins
+  for(i in 1: (na-1)){
+    X_older_aunt_uncle[,i+1] <- Uproj %*% prev_kin_older_aunts_uncles[,i]
+    X_older_cousins[,i+1] <- Uproj %*% prev_kin_older_cousins[,i] + Fproj %*% prev_kin_older_aunts_uncles[,i]
+    X_younger_cousins[,i+1] <- Uproj %*% prev_kin_younger_cousins[,i] + Fproj %*% prev_kin_younger_aunts_uncles[,i]
+  }
+
+  return(list("Focal" = X_Focal,
+              "d" = X_children,
+              "gd" = X_grand_children,
+              "ggd" = X_great_grand_children,
+              "m" = X_parents,
+              "gm" = X_grand_parents,
+              "ggm" = X_great_grand_parents,
+              "os" = X_older_sibs,
+              "ys" = X_younger_sibs,
+              "nos" = X_older_niece_nephew,
+              "nys" = X_younger_niece_nephew,
+              "oa" = X_older_aunt_uncle,
+              "ya" = X_younger_aunts_uncles,
+              "coa" = X_older_cousins,
+              "cya" = X_younger_cousins,
+              "ps" = population_age_stage_structure_next))
+}
+
+################## Create data frame output
+
+## Use of "pipe" (don't understand the name, but hey)
+`%>%` <- magrittr::`%>%`
+
+#' Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
+#'
+#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
+#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
+#' @param years vector. The timescale on which we implement the kinship model.
+#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
+#' @param na numeric. Number of ages.
+#' @param ns numeric. Number of stages.
+#' @param n_inc numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.
+#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
+#'
+#' @return A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
+#'
+
+create_cumsum_df <- function(kin_matrix_lists,
+                             kin_names,
+                             years,
+                             start_year,
+                             na,
+                             ns,
+                             n_inc,
+                             specific_kin){
+  df_year_list <- list()
+  for(j in years){
+    ii <- as.numeric(j) - start_year + 1
+    df_list <- list()
+    for(i in 1 : length(kin_names)){
+      kin_member <- kin_names[[i]]
+      kin_data <- kin_matrix_lists[[i]]
+      kin_data <- kin_data[[ii]]
+      df <- as.data.frame(as.matrix(kin_data))
+      dims <- dim( kin_data)
+      nr <- dims[1]
+      nc <- dims[2]
+      female_kin <- df[1:(nr/2), 1:nc]
+      male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      female_kin$stage <- rep(seq(1, ns), na)
+      male_kin$stage <- rep(seq(1, ns), na)
+      female_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
+      male_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
+      female_kin$Sex <- "Female"
+      male_kin$Sex <- "Male"
+      both_kin <- rbind(female_kin, male_kin)
+      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
+        dplyr::group_by(variable, stage, Sex) %>%
+        dplyr::summarise(num = sum(value)) %>%
+        dplyr::ungroup()
+      both_kin <- both_kin %>% dplyr::transmute(age_focal = variable,
+                                                stage_kin = as.factor(stage),
+                                                count = num,
+                                                sex_kin = Sex)
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal)) - 1
+      df <- both_kin
+      df$year <- j
+      df$group <- kin_member
+      df_list[[length(df_list)+1]] <- df
+    }
+    df_list <- do.call("rbind", df_list)
+    df_year_list[[(1+length(df_year_list))]] <- df_list
+  }
+  df_year_list <- do.call("rbind", df_year_list)
+  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
+                                                 cohort_factor = as.factor(cohort))
+  if(specific_kin != FALSE){
+    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
+  }
+  return(df_year_list)
+}
+
+#' Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
+#'
+#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
+#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
+#' @param years vector. The timescale on which we implement the kinship model.
+#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
+#' @param na numeric. Number of ages.
+#' @param ns numeric. Number of stages.
+#' @param n_inc numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.
+#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
+#'
+#' @return A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
+#'
+create_full_dists_df <- function(kin_matrix_lists,
+                                 kin_names,
+                                 years,
+                                 start_year,
+                                 na,
+                                 ns,
+                                 n_inc,
+                                 specific_kin){
+  df_year_list <- list()
+  for(j in years){
+    ii <- as.numeric(j) - start_year + 1
+    df_list <- list()
+    for(i in 1 : length(kin_names)){
+      kin_member <- kin_names[[i]]
+      kin_data <- kin_matrix_lists[[i]]
+      kin_data <- kin_data[[ii]]
+      df <- as.data.frame(as.matrix(kin_data))
+      dims <- dim( kin_data)
+      nr <- dims[1]
+      nc <- dims[2]
+      female_kin <- df[1:(nr/2), 1:nc]
+      male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      female_kin$stage <- rep(seq(1, ns), na)
+      male_kin$stage <- rep(seq(1, ns), na)
+      female_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
+      male_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
+      female_kin$Sex <- "Female"
+      male_kin$Sex <- "Male"
+      both_kin <- rbind(female_kin, male_kin)
+      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
+        dplyr::transmute(age_focal = variable,
+                         age_kin = age,
+                         stage_kin = as.factor(stage),
+                         count = value,
+                         sex_kin = Sex)
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal))-1
+      df <- both_kin
+      df$year <- j
+      df$group <- kin_member
+      df_list[[length(df_list)+1]] <- df
+    }
+    df_list <- do.call("rbind", df_list)
+    df_year_list[[(1+length(df_year_list))]] <- df_list
+  }
+  df_year_list <- do.call("rbind", df_year_list)
+  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
+                                                 cohort_factor = as.factor(cohort))
+  if(specific_kin != FALSE){
+    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
+  }
+  return(df_year_list)
+}
+
+
+
+## Construct a matrix composed as a direct sum of a list of matrices
+block_diag_function <- function(mat_list){
+  s = length(mat_list)
+  u1 = mat_list[[1]]
+  dims <- dim(u1)
+  r = dims[1]
+  diagmat <- Matrix::Matrix(nrow = (r*s), ncol = (r*s), data = 0, sparse = TRUE)
+  for(i in 1:s){
+    diagmat = diagmat + kronecker(E_matrix(i,i,s,s), mat_list[[i]])
+  }
+  return(diagmat)
+}
+
+## Construct a matrix which transfers Focal across stages, while ensuring Focal survives with probability 1
+get_G <- function(U, na, ns){
+  sig <- Matrix::t(rep(1,na*ns)) %*% U
+  diag <- Matrix::diag(sig[1,])
+  G <- U %*% MASS::ginv(diag)
+  return(G)
+}
+
+#' Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix <- function(Uf, Um, Ff, Fm, alpha, na, ns){
+  n <- length(Uf[1,])
+  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  F_block[1:n, 1:n] <- (1-alpha)*Ff
+  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
+  A <- block_diag_function(list(Uf,Um)) + F_block
+  stable_dist_vec <- SD(A)
+  ### Joint distributions
+  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  ### Age distributions
+  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
+  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
+  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+#' Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
+#'
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_TV <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
+  n <- length(Ff[1,])
+  ### Joint distributions
+  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  ### Age distributions
+  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
+  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
+  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+#' Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_parity <- function(Uf, Um, Ff, Fm, alpha, na, ns){
+  n <- length(Uf[1,])
+  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  F_block[1:n, 1:n] <- (1-alpha)*Ff
+  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
+  A <- block_diag_function(list(Uf,Um)) + F_block
+  stable_dist_vec <- SD(A)
+  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  m_mat <- pi_f %*% Matrix::t(rep(1,na))
+  d_mat <- pi_m %*% Matrix::t(rep(1,na))
+  pi_F <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
+  pi_M <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
+  for(i in 1:na){
+    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
+    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
+  }
+  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
+  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
+  ### Joint distributions
+  pi_f <- out_mum %*% pi_F
+  pi_m <- out_dad %*% pi_M
+  return(list(c(pi_f,pi_m), pi_f, pi_m, pi_F, pi_M))
+}
+
+#' Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_TV_parity <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
+  n <- length(Ff[1,])
+  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  m_mat <- pi_f %*% Matrix::t(rep(1,na))
+  d_mat <- pi_m %*% Matrix::t(rep(1,na))
+  pi_F  <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
+  pi_M <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
+  for(i in 1:na){
+    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
+    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
+  }
+  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
+  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
+  ### Joint distributions
+  pi_f <- out_mum %*% pi_F
+  pi_m <- out_dad %*% pi_M
+  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+
+######################################################### Some useful utility functions required
+
+
+###################################################### Eigen-decomposition of a matrix
+
+# Calculate the spectral radius of a matrix (growth rate in Demographics)
+lambda <- function(PM) {
+  lead_eig <- (abs(eigen(PM, only.values = TRUE)$values))
+  lead_eig <- lead_eig[which.max(lead_eig)]
+  return(lead_eig)
+}
+# Find the column-eigenvector corresponding to the spectral radius (Stable population structure in Demographics)
+SD <- function(PM) {
+  spectral_stuff <- eigen(PM)
+  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
+  # normalise...
+  vec_lambda <- spectral_stuff/sum(spectral_stuff)
+  return(vec_lambda)
+}
+# Find the row-eigenvector corresponding to the spectral radius (Stable reproductive values in Demographics)
+RD <- function(PM) {
+  spectral_stuff <- eigen(t(PM))
+  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
+  # normalise...
+  vec_lambda <- spectral_stuff/sum(spectral_stuff)
+  return(vec_lambda)
+}
+
+###################################################### Useful matrix operations
+
+## Constructing a unit vector with a 1 in the ith position
+e_vector <- function(i, n){
+  e <- rep(0, n)
+  e[i] <- 1
+  return(e)
+}
+## Creating a matrix of zeros with a 1 in the i,j-th entry
+E_matrix <- function(i,j,n,m){
+  E <- Matrix::Matrix(nrow = (n), ncol = (m), data = 0, sparse = TRUE)
+  E[i,j] <- 1
+  return(E)
+
+}
+## Creating the Vec-commutation matrix
+K_perm_mat <- function(n,m){
+  perm <- Matrix::Matrix(nrow = (n*m), ncol = (n*m), data = 0, sparse = TRUE)
+  for(i in 1:n){
+    for(j in 1:m){
+      perm = perm + kronecker( E_matrix(i,j,n,m) , Matrix::t(E_matrix(i,j,n,m)) )
+    }
+  }
+  return(perm)
+}
+
+
+
+
+
+
+
+
diff --git a/data/Female_parity_fert_list_UK.Rds b/data/Female_parity_fert_list_UK.Rds
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diff --git a/data/Redistribution_by_parity_list_UK.Rds b/data/Redistribution_by_parity_list_UK.Rds
new file mode 100644
index 0000000000000000000000000000000000000000..8603776b6b3bedca910216f9dfbec80b6bbd139d
GIT binary patch
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diff --git a/man/all_kin_dy.Rd b/man/all_kin_dy.Rd
new file mode 100644
index 0000000..261be62
--- /dev/null
+++ b/man/all_kin_dy.Rd
@@ -0,0 +1,48 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{all_kin_dy}
+\alias{all_kin_dy}
+\title{Title time invariant two-sex multi-state kin projections}
+\usage{
+all_kin_dy(
+  Uf,
+  Um,
+  Ff,
+  Fm,
+  alpha,
+  na,
+  ns,
+  Parity,
+  sex_Focal,
+  Initial_stage_Focal
+)
+}
+\arguments{
+\item{Uf}{matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)}
+
+\item{Um}{matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)}
+
+\item{Ff}{matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage}
+
+\item{Fm}{matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage}
+
+\item{alpha}{scalar. birth ratio (male:female)}
+
+\item{na}{scalar. number of ages.}
+
+\item{ns}{scalar. number of stages.}
+
+\item{Parity}{logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting}
+
+\item{sex_Focal}{logical. Female or Male}
+
+\item{Initial_stage_Focal}{numeric. Any natural number {1,2,3,4,...}}
+}
+\value{
+a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+yielding the age*stage distribution of kin for each age of Focal
+}
+\description{
+Title time invariant two-sex multi-state kin projections
+}
diff --git a/man/all_kin_dy_TV.Rd b/man/all_kin_dy_TV.Rd
new file mode 100644
index 0000000..c390385
--- /dev/null
+++ b/man/all_kin_dy_TV.Rd
@@ -0,0 +1,94 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{all_kin_dy_TV}
+\alias{all_kin_dy_TV}
+\title{Title time-variant two-sex multi-state kin projections}
+\usage{
+all_kin_dy_TV(
+  Uf,
+  Um,
+  Ff,
+  Fm,
+  alpha,
+  na,
+  ns,
+  Parity,
+  sex_Focal,
+  Initial_stage_Focal,
+  previous_kin_Focal,
+  prev_kin_children,
+  prev_kin_grandchildren,
+  prev_kin_greatgrandchildren,
+  prev_kin_parents,
+  prev_kin_grand_parents,
+  prev_kin_great_grand_parents,
+  prev_kin_older_sibs,
+  prev_kin_younger_sibs,
+  prev_kin_older_niece_nephew,
+  prev_kin_younger_niece_nephew,
+  prev_kin_older_aunts_uncles,
+  prev_kin_younger_aunts_uncles,
+  prev_kin_older_cousins,
+  prev_kin_younger_cousins,
+  previous_population_age_stage_structure
+)
+}
+\arguments{
+\item{Uf}{matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)}
+
+\item{Um}{matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)}
+
+\item{Ff}{matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage}
+
+\item{Fm}{matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage}
+
+\item{alpha}{scalar. birth ratio (male:female)}
+
+\item{na}{scalar. number of ages.}
+
+\item{ns}{scalar. number of stages.}
+
+\item{Parity}{logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting}
+
+\item{sex_Focal}{logical. Female or Male}
+
+\item{Initial_stage_Focal}{numeric. Any natural number {1,2,3,4,...}}
+
+\item{previous_kin_Focal}{matrix. last years kinship output.}
+
+\item{prev_kin_children}{matrix. last years kinship output.}
+
+\item{prev_kin_grandchildren}{matrix. last years kinship output.}
+
+\item{prev_kin_greatgrandchildren}{matrix. last years kinship output.}
+
+\item{prev_kin_parents}{matrix. last years kinship output.}
+
+\item{prev_kin_grand_parents}{matrix. last years kinship output.}
+
+\item{prev_kin_older_sibs}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_sibs}{matrix. last years kinship output.}
+
+\item{prev_kin_older_niece_nephew}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_niece_nephew}{matrix. last years kinship output.}
+
+\item{prev_kin_older_aunts_uncles}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_aunts_uncles}{matrix. last years kinship output.}
+
+\item{prev_kin_older_cousins}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_cousins}{matrix. last years kinship output.}
+
+\item{previous_population_age_stage_structure}{vector. The transient "population structure" (age*stage distributed)}
+}
+\value{
+a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+yielding the age*stage distribution of kin for each age of Focal
+}
+\description{
+Title time-variant two-sex multi-state kin projections
+}
diff --git a/man/create_cumsum_df.Rd b/man/create_cumsum_df.Rd
new file mode 100644
index 0000000..ea72c96
--- /dev/null
+++ b/man/create_cumsum_df.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{create_cumsum_df}
+\alias{create_cumsum_df}
+\title{Title Accumulated kin by each age of Focal, for each time period, and cohort of birth}
+\usage{
+create_cumsum_df(
+  kin_matrix_lists,
+  kin_names,
+  years,
+  start_year,
+  na,
+  ns,
+  n_inc,
+  specific_kin
+)
+}
+\arguments{
+\item{kin_matrix_lists}{list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+so list(X_focal) = list(X_focal\link{year1},X_focal\link{year2},...,X_focal\link{yearlast})}
+
+\item{kin_names}{list of characters. Corresponding to above lists: list("F","m",....)}
+
+\item{years}{vector. The timescale on which we implement the kinship model.}
+
+\item{start_year}{. First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)}
+
+\item{na}{numeric. Number of ages.}
+
+\item{ns}{numeric. Number of stages.}
+
+\item{n_inc}{numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.}
+
+\item{specific_kin}{character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.}
+}
+\value{
+A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
+}
+\description{
+Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
+}
diff --git a/man/create_full_dists_df.Rd b/man/create_full_dists_df.Rd
new file mode 100644
index 0000000..a8cd151
--- /dev/null
+++ b/man/create_full_dists_df.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{create_full_dists_df}
+\alias{create_full_dists_df}
+\title{Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth}
+\usage{
+create_full_dists_df(
+  kin_matrix_lists,
+  kin_names,
+  years,
+  start_year,
+  na,
+  ns,
+  n_inc,
+  specific_kin
+)
+}
+\arguments{
+\item{kin_matrix_lists}{list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+so list(X_focal) = list(X_focal\link{year1},X_focal\link{year2},...,X_focal\link{yearlast})}
+
+\item{kin_names}{list of characters. Corresponding to above lists: list("F","m",....)}
+
+\item{years}{vector. The timescale on which we implement the kinship model.}
+
+\item{start_year}{. First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)}
+
+\item{na}{numeric. Number of ages.}
+
+\item{ns}{numeric. Number of stages.}
+
+\item{n_inc}{numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.}
+
+\item{specific_kin}{character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.}
+}
+\value{
+A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
+}
+\description{
+Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
+}
diff --git a/man/kin_multi_stage_time_variant_2sex.Rd b/man/kin_multi_stage_time_variant_2sex.Rd
new file mode 100644
index 0000000..8eadc67
--- /dev/null
+++ b/man/kin_multi_stage_time_variant_2sex.Rd
@@ -0,0 +1,63 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{kin_multi_stage_time_variant_2sex}
+\alias{kin_multi_stage_time_variant_2sex}
+\title{Estimate kin counts by age, stage, and sex, in a time variant framework}
+\usage{
+kin_multi_stage_time_variant_2sex(
+  U_list_females = NULL,
+  U_list_males = NULL,
+  F_list_females = NULL,
+  F_list_males = NULL,
+  T_list_females = NULL,
+  T_list_males = NULL,
+  H_list = NULL,
+  birth_female = 0.49,
+  parity = FALSE,
+  output_kin = FALSE,
+  summary_kin = TRUE,
+  sex_Focal = "Female",
+  initial_stage_Focal = NULL,
+  n_inc = NULL,
+  output_years
+)
+}
+\arguments{
+\item{U_list_females}{list with matrix entries: period-specific female survival probabilities. Age in rows and states in columns.}
+
+\item{U_list_males}{list with matrix entries: period-specific male survival probabilities. Age in rows and states in columns.}
+
+\item{F_list_females}{list with matrix with elements: period-specific female fertility (age in rows and states in columns).}
+
+\item{F_list_males}{list with matrix entries: period-specific male fertility (age in rows and states in columns).}
+
+\item{T_list_females}{list of lists with matrix entries: each outer list entry is period-specific, and composed of
+a list of stochastic matrices which describe age-specific female probabilities of transferring stage}
+
+\item{T_list_males}{list of lists with matrix entries: each outer list entry is period-specific, and composed of
+a list of stochastic matrices which describe age-specific male probabilities of transferring stage}
+
+\item{H_list}{list with matrix entries: redistribution of newborns across each stage to a specific age-class}
+
+\item{birth_female}{numeric. ratio of males to females in population}
+
+\item{parity}{logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default \code{TRUE}.}
+
+\item{output_kin}{vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.}
+
+\item{summary_kin}{logical. Results as a data frame of accumulated kin by age of Focal if FALSE, and kin by their age*stage distribution by age of Focal if TRUE.}
+
+\item{sex_Focal}{character. Female or Male as the user requests}
+
+\item{initial_stage_Focal}{Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)}
+
+\item{n_inc}{numeric. The age/time-increment used in the discretisation of the continuum.}
+
+\item{output_years}{vector. The times at which we wish to count kin: start year = output_years\link{1}, and end year = output_years\link{length.}}
+}
+\value{
+A data frame with focal´s age, related ages, stages, sexes, and types of kin for each time-period
+}
+\description{
+Implementation of combined formal demographic models: Caswell II,III,IV.
+}
diff --git a/man/pi_mix.Rd b/man/pi_mix.Rd
new file mode 100644
index 0000000..0ed3c83
--- /dev/null
+++ b/man/pi_mix.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix}
+\alias{pi_mix}
+\title{Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case}
+\usage{
+pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
+}
+\arguments{
+\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
+
+\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
+
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
+}
diff --git a/man/pi_mix_TV.Rd b/man/pi_mix_TV.Rd
new file mode 100644
index 0000000..5f39775
--- /dev/null
+++ b/man/pi_mix_TV.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix_TV}
+\alias{pi_mix_TV}
+\title{Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case}
+\usage{
+pi_mix_TV(Ff, Fm, alpha, na, ns, previous_age_stage_dist)
+}
+\arguments{
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+
+\item{previous_age_stage_dist}{vector. Last years population structure (age\emph{stage}sex full distribution)}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
+}
diff --git a/man/pi_mix_TV_parity.Rd b/man/pi_mix_TV_parity.Rd
new file mode 100644
index 0000000..9a95ab8
--- /dev/null
+++ b/man/pi_mix_TV_parity.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix_TV_parity}
+\alias{pi_mix_TV_parity}
+\title{Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case}
+\usage{
+pi_mix_TV_parity(Ff, Fm, alpha, na, ns, previous_age_stage_dist)
+}
+\arguments{
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+
+\item{previous_age_stage_dist}{vector. Last years population structure (age\emph{stage}sex full distribution)}
+
+\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
+
+\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
+}
diff --git a/man/pi_mix_parity.Rd b/man/pi_mix_parity.Rd
new file mode 100644
index 0000000..f3874c8
--- /dev/null
+++ b/man/pi_mix_parity.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix_parity}
+\alias{pi_mix_parity}
+\title{Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case}
+\usage{
+pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
+}
+\arguments{
+\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
+
+\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
+
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
+}
diff --git a/tests/testthat/test-kin_twosex_multistate_timevariant.R b/tests/testthat/test-kin_twosex_multistate_timevariant.R
new file mode 100644
index 0000000..d89b8e7
--- /dev/null
+++ b/tests/testthat/test-kin_twosex_multistate_timevariant.R
@@ -0,0 +1,77 @@
+
+
+
+
+
+test_that("same output in multi_stage (caswell 2020)", {
+  load("data/svk_Uxs.rda")
+  Tf <- unname(svk_Uxs)
+  Tm <- unname(svk_Uxs)
+  load("data/svk_fxs.rda")
+  Ff <- unname(svk_fxs)
+  Fm <- unname(svk_fxs)
+  Ff <- (1/0.49)*Ff
+  Fm <- (1/0.49)*Fm
+  load("data/svk_pxs.rda")
+  Uf <- unname(svk_pxs)
+  Um <- unname(svk_pxs)
+  load("data/svk_Hxs.rda")
+  H <- unname(svk_Hxs)
+
+  load("data/kin_svk1990_caswell2020.rda")
+
+  joe_output <- kin_multi_stage_time_variant_2sex(list(Uf),
+                                                  list(Um),
+                                                  list(Ff),
+                                                  list(Fm),
+                                                  list(Tf),
+                                                  list(Tm),
+                                                  list(H),
+                                                  birth_female = 0.49, ## svk_fxs already divided
+                                                  output_kin = FALSE,
+                                                  parity = TRUE,
+                                                  summary_kin = FALSE,
+                                                  sex_Focal = "Female", ##  define Focal's sex at birth
+                                                  initial_stage_Focal = 1, ## Define Focal's stage at birth
+                                                  n_inc = 1, # width of age class
+                                                  seq(1990, (1990)))
+
+
+  ## Younger sisters
+  jcmp_ys <- joe_output %>% dplyr::filter(sex_kin == "Female", group == "ys") %>%
+    dplyr::select(age_focal, age_kin, stage_kin, count) %>%
+    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count)
+  hals_output_ys <- kin_svk1990_caswell2020$ys
+  hals_output_ys <- as.data.frame(hals_output_ys)
+  colnames(hals_output_ys) <- seq(0,109,1)
+  hals_output_ys$age_kin <- rep(seq(0, (110-1), 1), each = 6)
+  hals_output_ys$stage_kin <- rep(seq(1, 6), 110)
+  hcmp_ys <- hals_output_ys %>% reshape2::melt(id = c("age_kin","stage_kin")) %>%
+    dplyr::mutate(age_focal = variable,
+                  count = value) %>%
+    dplyr::select(age_kin, stage_kin, age_focal, count) %>%
+    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count)
+
+  ## Older sisters
+  jcmp_os <- joe_output %>% dplyr::filter(sex_kin == "Female", group == "os") %>%
+    dplyr::select(age_focal, age_kin, stage_kin, count) %>%
+    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count)
+  hals_output_os <- kin_svk1990_caswell2020$os
+  hals_output_os <- as.data.frame(hals_output_os)
+  colnames(hals_output_os) <- seq(0,109,1)
+  hals_output_os$age_kin <- rep(seq(0, (110-1), 1), each = 6)
+  hals_output_os$stage_kin <- rep(seq(1, 6), 110)
+  hcmp_os <- hals_output_os %>% reshape2::melt(id = c("age_kin","stage_kin")) %>%
+    dplyr::mutate(age_focal = variable,
+                  count = value) %>%
+    dplyr::select(age_kin, stage_kin, age_focal, count) %>%
+    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count)
+
+
+
+
+  expect_equal(jcmp_ys$count, hcmp_ys$count)
+  expect_equal(jcmp_os$count, hcmp_os$count)
+
+
+})
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
new file mode 100644
index 0000000..239ba5c
--- /dev/null
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -0,0 +1,255 @@
+---
+  title: "Expected kin counts by type of relative in a two-sex multi-state time-varying framework"
+output:
+  html_document:
+  toc: true
+toc_depth: 1
+vignette: >
+  %\VignetteEngine{knitr::rmarkdown}
+%\VignetteEncoding{UTF-8}
+---
+
+  Note that this is currently an R project and not package. To run code in this vignette we need to run/source all files in the folder ``Matrix Model''.
+
+```{R}
+library(DemoKin)
+library(Matrix)
+library(tictoc)
+`%>%` <- magrittr::`%>%`
+
+
+options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise output (but also #lose #progress bar)
+
+```
+
+In this vignette, we'll demonstrate how `Two_Sex_Time_Varying_MultiState_Kinship` can be used to compute parity-specific kinship networks for an average member of a population, the sex of whom is user specified, and who is subject to time-varying demographic rates. We call this individual Focal. We seek the number, age, and parity distribution of Focal's relatives for each age of Focal's life as a function of the year in which Focal is born.
+
+### Kin counts by parity in a two sex time variant demography ###
+
+In this example we use data for the UK ranging from 1965 - 2005, sourced from the [Human Mortality Database](https://www.mortality.org/) and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). Some simplifying assumptions we make due to data availability are as follows:
+
+i) Fertility rates vary with time, are distinct among parity class, but the same over sexes (the so-called ``androgynous approximation'').
+ii) Mortality rates vary with time and are distinct across sex, but are the same over parity classes (no parity-specific mortality)
+iii) The age-specific probabilities of parity-progression vary with time, but are the same over sex (androgynous approximation again)
+
+In order to implement the model, the function `kin_multi_stage_TV_2_sex` expects the following 7 inputs, fed in as lists:
+
+1) LIST: Female age-and-parity specific survival probabilities over the timescale.
+This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
+
+2) LIST: Male age-and-parity specific survival probabilities over the timescale.
+This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
+
+3) LIST: Female age-and-parity specific fertility rates over the timescale.
+This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
+
+4) LIST: Male age-and-parity specific fertility rates over the timescale.
+This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
+
+5) OUTER LIST with INNER LISTS: Female age-specific probabilities of moving up parity over the timescale.
+The outer list has length = the timescale. The inner list has length = number of ages. Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage. Thus for each year, we have a list of age-specific probabilities of moving from one stage to the next.
+
+6) Same as 5) but for males
+
+7) LIST: Length = timescale, and each element is a matrix which assigns the offspring of individuals in some stage to the appropriate age class (age in rows and states in columns)
+
+To avoid further calculations these lists are constructed in another file and simply imported below (See ``Examples'' , ``Matrix_construction'' for details). The code below reads in the above function input lists.
+
+```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
+
+# Lets construct these lists as model inputs..............
+
+F_mat_fem <- readr::read_rds(here::here("data","Female_parity_fert_list_UK.Rds"))
+F_mat_male <- readr::read_rds(here::here("data","Male_parity_fert_list_UK.Rds"))
+T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_UK.Rds"))
+T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_UK.Rds"))
+U_mat_fem <- readr::read_rds(here::here("data","Female_parity_mortality_list_UK.Rds"))
+U_mat_male <- readr::read_rds(here::here("data","Male_parity_mortality_list_UK.Rds"))
+H_mat <- readr::read_rds(here::here("data","Redistribution_by_parity_list_UK.Rds"))
+
+```
+
+Recap: above are lists of period-specific demographic rates, in particular comprising:
+
+U_mat_fem: list of age by stage matrices, entries give female probability of survival.
+List starting 1965 ending 2022.
+U_mat_male: list of age by stage matrices, entries give female probability of survival.
+List starting 1965 ending 2022.
+F_mat_fem: list of age by stage matrices, entries give female fert,
+List starting 1965 ending 2022.
+F_mat_male == F_mat_fem.
+T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities a female moves up parity (inner list has length of number of age-classes).
+Outer list starting 1965 ending 2022
+T_mat_male == T_mat_fem.
+H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
+
+We feed these inputs into the matrix model, along with other arguments:
+UK sex ratio --> alpha = 0.51
+We want all of Focal's kin network --> specific_kin = F
+We are considering parity --> parity = T
+Focal is female --> sex_Focal = "Female"
+Accumulated kin in this example --> dist_output = FALSE
+Focal born into parity 0 --> stage_Focal = 1
+age-increments by 1 year --> nc = 1
+timescale from 1965-1985 -- > seq(1965, 1965 + 40)
+
+### 1. For now lets simply consider the accumulated number of kin Focal expects over the lifecourse ###
+
+```{r, message=FALSE, warning=FALSE}
+# Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
+no_years <- 40
+# and we start projecting kin in 1965
+# We decide here to count accumulated kin by age of Focal, and not distributions of kin
+kin_out_1965_2005 <-
+  kin_multi_stage_time_variant_2sex(U_mat_fem[1:(1+no_years)],
+                                    U_mat_male[1:(1+no_years)],
+                                    F_mat_fem[1:(1+no_years)],
+                                    F_mat_male[1:(1+no_years)],
+                                    T_mat_fem[1:(1+no_years)],
+                                    T_mat_fem[1:(1+no_years)],
+                                    H_mat[1:(1+no_years)],
+                                    birth_female = 1 - 0.51, ## Sex ratio -- UK value
+                                    parity = TRUE,
+                                    output_kin = FALSE,
+                                    summary_kin = TRUE,
+                                    sex_Focal = "Female", ##  define Focal's sex at birth
+                                    initial_stage_Focal = 1, ## Define Focal's stage at birth
+                                    n_inc = 1, # width of age class
+                                    output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
+  )
+```
+### 1.1. Visualizing kin ###
+
+### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
+
+For an example, let's say we really want to understand the age*parity distributions of the accumulated number of aunts and uncles older than Focal's mother and father, for each age of Focal, and for each period in time from 1965-1985. Some people will do....
+
+```{r, fig.height=6, fig.width=8}
+kin_out_1965_2005 %>%
+  dplyr::filter(group == "oa",
+                year %in% c(1965, 1975, 1985, 1995, 2005)) %>%
+  ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
+  ggplot2::geom_bar(position = "stack", stat = "identity") +
+  ggplot2::facet_grid(sex_kin ~ year) +
+  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  ggplot2::theme_bw() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
+  ggplot2::ylab("Older aunts and uncles")
+```
+We could also consider any other kin in Focal's network, for instance, offspring
+
+```{r, fig.height=6, fig.width=8}
+kin_out_1965_2005 %>%
+  dplyr::filter(group == "d",
+                year %in% c(1965, 1975, 1985, 1995, 2005)) %>%
+  ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
+  ggplot2::geom_bar(position = "stack", stat = "identity") +
+  ggplot2::facet_grid(sex_kin ~ year) +
+  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  ggplot2::theme_bw() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
+  ggplot2::ylab("Offspring")
+```
+### 1.1.2. Plotting the kin of Focal as a function of Focal's cohort of birth ####
+
+Since we only ran the model for 40 years (between 1968-2005), there is very little scope to view kinship as cohort-specific. We can however compare cohorts for 40-year segments of Focal's life.
+Below, following from the above example, we once again consider offspring
+
+```{r, fig.height = 6, fig.width = 8}
+kin_out_1965_2005 %>%
+  dplyr::filter(group == "d", cohort %in% c(1910,1925,1965) ) %>%
+  ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
+  ggplot2::geom_bar(position = "stack", stat = "identity") +
+  ggplot2::facet_grid(sex_kin ~ cohort)  +
+  ggplot2::theme_bw() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
+  ggplot2::ylab("Offspring")
+```
+The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
+
+The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in partity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by a well mixed parity-distribution at this age of Focal.
+
+the RHS plot (1965 cohort) simply reflects the fact that Focal will not start reproduction until around 15 years old.
+
+### 2. Now lets consider the distributions of kin Focal expects over the lifecourse ###
+
+To obtain distributions of kin as output, we simply change the function argument: dist_output = TRUE
+
+```{r, message=FALSE, warning=FALSE}
+rm(kin_out_1965_2005)
+gc()
+no_years <- 40
+
+kin_out_1965_2005_full <-
+  kin_multi_stage_time_variant_2sex(U_mat_fem[1:(1+no_years)],
+                                    U_mat_male[1:(1+no_years)],
+                                    F_mat_fem[1:(1+no_years)],
+                                    F_mat_male[1:(1+no_years)],
+                                    T_mat_fem[1:(1+no_years)],
+                                    T_mat_fem[1:(1+no_years)],
+                                    H_mat[1:(1+no_years)],
+                                    birth_female = 1 - 0.51, ## Sex ratio -- UK value
+                                    parity = TRUE,
+                                    output_kin = FALSE,
+                                    summary_kin = FALSE,
+                                    sex_Focal = "Female", ##  define Focal's sex at birth
+initial_stage_Focal = 1, ## Define Focal's stage at birth
+n_inc = 1, # width of age class
+output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
+)
+```
+### 2.1. Visualizing kin ###
+
+Let us now visualize the distribution of relatives over Focal's lifecourse using the model output
+### 2.1.1. Plotting kin distributions for an average Focal of fixed age, at some fixed period in time ###
+
+Below I plot the expected age*stage distribution of an average Focal's younger siblings over the years 1965, 1975, 1085, 1995, and 2005, given Focal is of age 50
+
+```{r, fig.height = 6, fig.width = 8}
+kin_out_1965_2005_full %>%
+  dplyr::filter(group == "ys",
+                year %in% c(1965, 1975, 1985, 1995, 2005),
+                age_focal == 50) %>%
+  ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
+  ggplot2::geom_bar(position = "stack", stat = "identity") +
+  ggplot2::facet_grid(sex_kin ~ year) +
+  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  ggplot2::theme_bw() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
+  ggplot2::ylab("Younger siblings") +
+  ggplot2::ggtitle("Focal 50")
+```
+As expected, the discontinuity reflects the fact that Focal's younger siblings cannot be of age >=50. Contrastingly, when we look at the age*stage distribution of older siblings, the discontinuity bounds kin to be of age >50, as plotted below
+
+```{r, fig.height = 6, fig.width = 8}
+kin_out_1965_2005_full %>%
+  dplyr::filter(group == "os",
+                year %in% c(1965, 1975, 1985, 1995, 2005),
+                age_focal == 50) %>%
+  ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
+  ggplot2::geom_bar(position = "stack", stat = "identity") +
+  ggplot2::facet_grid(sex_kin ~ year) +
+  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  ggplot2::theme_bw() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
+  ggplot2::ylab("Older siblings") +
+  ggplot2::ggtitle("Focal 50")
+```
+With a simple bit of playing with the output data frame, we can plot the age*stage distribution of the combined siblings of Focal
+
+```{r, fig.height = 6, fig.width = 8}
+kin_out_1965_2005_full %>%
+  dplyr::filter((group == "ys" | group == "os"),
+                year %in% c(1965, 1975, 1985, 1995, 2005),
+                age_focal == 50) %>%
+  tidyr::pivot_wider(names_from = group, values_from = count) %>%
+  dplyr::mutate(count = `ys` + `os`) %>%
+  ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
+  ggplot2::geom_bar(position = "stack", stat = "identity") +
+  ggplot2::facet_grid(sex_kin ~ year) +
+  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  ggplot2::theme_bw() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
+  ggplot2::ylab("All siblings") +
+  ggplot2::ggtitle("Focal 50")
+```

From 9aec5b480c6bb8e69f72af426ee5cb5ca2b8075b Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 15:53:42 +0100
Subject: [PATCH 39/89] removing function documentation -- optional

---
 R/kin_multi_stage_time_variant_2sex.R |  6 +-
 man/all_kin_dy.Rd                     | 48 --------------
 man/all_kin_dy_TV.Rd                  | 94 ---------------------------
 man/create_cumsum_df.Rd               | 41 ------------
 man/create_full_dists_df.Rd           | 41 ------------
 man/pi_mix.Rd                         | 33 ----------
 man/pi_mix_TV.Rd                      | 31 ---------
 man/pi_mix_TV_parity.Rd               | 35 ----------
 man/pi_mix_parity.Rd                  | 33 ----------
 9 files changed, 1 insertion(+), 361 deletions(-)
 delete mode 100644 man/all_kin_dy.Rd
 delete mode 100644 man/all_kin_dy_TV.Rd
 delete mode 100644 man/create_cumsum_df.Rd
 delete mode 100644 man/create_full_dists_df.Rd
 delete mode 100644 man/pi_mix.Rd
 delete mode 100644 man/pi_mix_TV.Rd
 delete mode 100644 man/pi_mix_TV_parity.Rd
 delete mode 100644 man/pi_mix_parity.Rd

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index c930147..e2842f2 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -301,9 +301,7 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
 #' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
 #' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
 #' yielding the age*stage distribution of kin for each age of Focal
-#'
-#'
-#'
+
 all_kin_dy <- function(Uf,
                        Um,
                        Ff,
@@ -510,7 +508,6 @@ all_kin_dy <- function(Uf,
 #' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
 #' yielding the age*stage distribution of kin for each age of Focal
 #'
-#'
 all_kin_dy_TV <- function(Uf,
                           Um,
                           Ff,
@@ -706,7 +703,6 @@ all_kin_dy_TV <- function(Uf,
 #'
 #' @return A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
 #'
-
 create_cumsum_df <- function(kin_matrix_lists,
                              kin_names,
                              years,
diff --git a/man/all_kin_dy.Rd b/man/all_kin_dy.Rd
deleted file mode 100644
index 261be62..0000000
--- a/man/all_kin_dy.Rd
+++ /dev/null
@@ -1,48 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{all_kin_dy}
-\alias{all_kin_dy}
-\title{Title time invariant two-sex multi-state kin projections}
-\usage{
-all_kin_dy(
-  Uf,
-  Um,
-  Ff,
-  Fm,
-  alpha,
-  na,
-  ns,
-  Parity,
-  sex_Focal,
-  Initial_stage_Focal
-)
-}
-\arguments{
-\item{Uf}{matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)}
-
-\item{Um}{matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)}
-
-\item{Ff}{matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage}
-
-\item{Fm}{matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage}
-
-\item{alpha}{scalar. birth ratio (male:female)}
-
-\item{na}{scalar. number of ages.}
-
-\item{ns}{scalar. number of stages.}
-
-\item{Parity}{logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting}
-
-\item{sex_Focal}{logical. Female or Male}
-
-\item{Initial_stage_Focal}{numeric. Any natural number {1,2,3,4,...}}
-}
-\value{
-a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
-nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
-yielding the age*stage distribution of kin for each age of Focal
-}
-\description{
-Title time invariant two-sex multi-state kin projections
-}
diff --git a/man/all_kin_dy_TV.Rd b/man/all_kin_dy_TV.Rd
deleted file mode 100644
index c390385..0000000
--- a/man/all_kin_dy_TV.Rd
+++ /dev/null
@@ -1,94 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{all_kin_dy_TV}
-\alias{all_kin_dy_TV}
-\title{Title time-variant two-sex multi-state kin projections}
-\usage{
-all_kin_dy_TV(
-  Uf,
-  Um,
-  Ff,
-  Fm,
-  alpha,
-  na,
-  ns,
-  Parity,
-  sex_Focal,
-  Initial_stage_Focal,
-  previous_kin_Focal,
-  prev_kin_children,
-  prev_kin_grandchildren,
-  prev_kin_greatgrandchildren,
-  prev_kin_parents,
-  prev_kin_grand_parents,
-  prev_kin_great_grand_parents,
-  prev_kin_older_sibs,
-  prev_kin_younger_sibs,
-  prev_kin_older_niece_nephew,
-  prev_kin_younger_niece_nephew,
-  prev_kin_older_aunts_uncles,
-  prev_kin_younger_aunts_uncles,
-  prev_kin_older_cousins,
-  prev_kin_younger_cousins,
-  previous_population_age_stage_structure
-)
-}
-\arguments{
-\item{Uf}{matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)}
-
-\item{Um}{matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)}
-
-\item{Ff}{matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage}
-
-\item{Fm}{matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage}
-
-\item{alpha}{scalar. birth ratio (male:female)}
-
-\item{na}{scalar. number of ages.}
-
-\item{ns}{scalar. number of stages.}
-
-\item{Parity}{logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting}
-
-\item{sex_Focal}{logical. Female or Male}
-
-\item{Initial_stage_Focal}{numeric. Any natural number {1,2,3,4,...}}
-
-\item{previous_kin_Focal}{matrix. last years kinship output.}
-
-\item{prev_kin_children}{matrix. last years kinship output.}
-
-\item{prev_kin_grandchildren}{matrix. last years kinship output.}
-
-\item{prev_kin_greatgrandchildren}{matrix. last years kinship output.}
-
-\item{prev_kin_parents}{matrix. last years kinship output.}
-
-\item{prev_kin_grand_parents}{matrix. last years kinship output.}
-
-\item{prev_kin_older_sibs}{matrix. last years kinship output.}
-
-\item{prev_kin_younger_sibs}{matrix. last years kinship output.}
-
-\item{prev_kin_older_niece_nephew}{matrix. last years kinship output.}
-
-\item{prev_kin_younger_niece_nephew}{matrix. last years kinship output.}
-
-\item{prev_kin_older_aunts_uncles}{matrix. last years kinship output.}
-
-\item{prev_kin_younger_aunts_uncles}{matrix. last years kinship output.}
-
-\item{prev_kin_older_cousins}{matrix. last years kinship output.}
-
-\item{prev_kin_younger_cousins}{matrix. last years kinship output.}
-
-\item{previous_population_age_stage_structure}{vector. The transient "population structure" (age*stage distributed)}
-}
-\value{
-a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
-nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
-yielding the age*stage distribution of kin for each age of Focal
-}
-\description{
-Title time-variant two-sex multi-state kin projections
-}
diff --git a/man/create_cumsum_df.Rd b/man/create_cumsum_df.Rd
deleted file mode 100644
index ea72c96..0000000
--- a/man/create_cumsum_df.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{create_cumsum_df}
-\alias{create_cumsum_df}
-\title{Title Accumulated kin by each age of Focal, for each time period, and cohort of birth}
-\usage{
-create_cumsum_df(
-  kin_matrix_lists,
-  kin_names,
-  years,
-  start_year,
-  na,
-  ns,
-  n_inc,
-  specific_kin
-)
-}
-\arguments{
-\item{kin_matrix_lists}{list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
-so list(X_focal) = list(X_focal\link{year1},X_focal\link{year2},...,X_focal\link{yearlast})}
-
-\item{kin_names}{list of characters. Corresponding to above lists: list("F","m",....)}
-
-\item{years}{vector. The timescale on which we implement the kinship model.}
-
-\item{start_year}{. First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)}
-
-\item{na}{numeric. Number of ages.}
-
-\item{ns}{numeric. Number of stages.}
-
-\item{n_inc}{numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.}
-
-\item{specific_kin}{character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.}
-}
-\value{
-A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
-}
-\description{
-Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
-}
diff --git a/man/create_full_dists_df.Rd b/man/create_full_dists_df.Rd
deleted file mode 100644
index a8cd151..0000000
--- a/man/create_full_dists_df.Rd
+++ /dev/null
@@ -1,41 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{create_full_dists_df}
-\alias{create_full_dists_df}
-\title{Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth}
-\usage{
-create_full_dists_df(
-  kin_matrix_lists,
-  kin_names,
-  years,
-  start_year,
-  na,
-  ns,
-  n_inc,
-  specific_kin
-)
-}
-\arguments{
-\item{kin_matrix_lists}{list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
-so list(X_focal) = list(X_focal\link{year1},X_focal\link{year2},...,X_focal\link{yearlast})}
-
-\item{kin_names}{list of characters. Corresponding to above lists: list("F","m",....)}
-
-\item{years}{vector. The timescale on which we implement the kinship model.}
-
-\item{start_year}{. First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)}
-
-\item{na}{numeric. Number of ages.}
-
-\item{ns}{numeric. Number of stages.}
-
-\item{n_inc}{numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.}
-
-\item{specific_kin}{character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.}
-}
-\value{
-A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
-}
-\description{
-Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
-}
diff --git a/man/pi_mix.Rd b/man/pi_mix.Rd
deleted file mode 100644
index 0ed3c83..0000000
--- a/man/pi_mix.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{pi_mix}
-\alias{pi_mix}
-\title{Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case}
-\usage{
-pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
-}
-\arguments{
-\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
-
-\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
-
-\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
-
-\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
-
-\item{alpha}{scalar. Birth ratio male:female}
-
-\item{na}{scalar. Number of age-classes}
-
-\item{ns}{scalar. Number of stages}
-}
-\value{
-list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
-list[\link{2}] = female age\emph{stage distribution normalised
-list[\link{3}] = male age}stage distribution normalised
-list[\link{4}] = female marginal age distribution normalised
-list[\link{5}] = male marginal age distribution normalised
-}
-\description{
-Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
-}
diff --git a/man/pi_mix_TV.Rd b/man/pi_mix_TV.Rd
deleted file mode 100644
index 5f39775..0000000
--- a/man/pi_mix_TV.Rd
+++ /dev/null
@@ -1,31 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{pi_mix_TV}
-\alias{pi_mix_TV}
-\title{Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case}
-\usage{
-pi_mix_TV(Ff, Fm, alpha, na, ns, previous_age_stage_dist)
-}
-\arguments{
-\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
-
-\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
-
-\item{alpha}{scalar. Birth ratio male:female}
-
-\item{na}{scalar. Number of age-classes}
-
-\item{ns}{scalar. Number of stages}
-
-\item{previous_age_stage_dist}{vector. Last years population structure (age\emph{stage}sex full distribution)}
-}
-\value{
-list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
-list[\link{2}] = female age\emph{stage distribution normalised
-list[\link{3}] = male age}stage distribution normalised
-list[\link{4}] = female marginal age distribution normalised
-list[\link{5}] = male marginal age distribution normalised
-}
-\description{
-Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
-}
diff --git a/man/pi_mix_TV_parity.Rd b/man/pi_mix_TV_parity.Rd
deleted file mode 100644
index 9a95ab8..0000000
--- a/man/pi_mix_TV_parity.Rd
+++ /dev/null
@@ -1,35 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{pi_mix_TV_parity}
-\alias{pi_mix_TV_parity}
-\title{Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case}
-\usage{
-pi_mix_TV_parity(Ff, Fm, alpha, na, ns, previous_age_stage_dist)
-}
-\arguments{
-\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
-
-\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
-
-\item{alpha}{scalar. Birth ratio male:female}
-
-\item{na}{scalar. Number of age-classes}
-
-\item{ns}{scalar. Number of stages}
-
-\item{previous_age_stage_dist}{vector. Last years population structure (age\emph{stage}sex full distribution)}
-
-\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
-
-\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
-}
-\value{
-list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
-list[\link{2}] = female age\emph{stage distribution normalised
-list[\link{3}] = male age}stage distribution normalised
-list[\link{4}] = female marginal age distribution normalised
-list[\link{5}] = male marginal age distribution normalised
-}
-\description{
-Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
-}
diff --git a/man/pi_mix_parity.Rd b/man/pi_mix_parity.Rd
deleted file mode 100644
index f3874c8..0000000
--- a/man/pi_mix_parity.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
-\name{pi_mix_parity}
-\alias{pi_mix_parity}
-\title{Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case}
-\usage{
-pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
-}
-\arguments{
-\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
-
-\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
-
-\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
-
-\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
-
-\item{alpha}{scalar. Birth ratio male:female}
-
-\item{na}{scalar. Number of age-classes}
-
-\item{ns}{scalar. Number of stages}
-}
-\value{
-list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
-list[\link{2}] = female age\emph{stage distribution normalised
-list[\link{3}] = male age}stage distribution normalised
-list[\link{4}] = female marginal age distribution normalised
-list[\link{5}] = male marginal age distribution normalised
-}
-\description{
-Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
-}

From 8dd7ad7830c1c753abf6e3ec9dfd7776f1c8b004 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 15:55:33 +0100
Subject: [PATCH 40/89] using tictoc in DESC

---
 DESCRIPTION | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 62a4398..9dd070e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -32,7 +32,8 @@ Imports:
     igraph,
     magrittr,
     data.table,
-    lifecycle
+    lifecycle,
+    tictoc
 URL: https://github.com/IvanWilli/DemoKin
 BugReports: https://github.com/IvanWilli/DemoKin/issues
 Depends: 

From 7d633aa53a3eddff08b0a51ba1fb72d86741b004 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 16:15:25 +0100
Subject: [PATCH 41/89] removing n_inc

---
 R/kin_multi_stage_time_variant_2sex.R            | 16 ++++------------
 man/kin_multi_stage_time_variant_2sex.Rd         |  3 ---
 .../test-kin_twosex_multistate_timevariant.R     |  1 -
 .../Reference_TwoSex_MultiState_TimeVariant.Rmd  |  6 ++----
 4 files changed, 6 insertions(+), 20 deletions(-)

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index e2842f2..f3e3d6c 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -19,7 +19,6 @@
 #' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if FALSE, and kin by their age*stage distribution by age of Focal if TRUE.
 #' @param sex_Focal character. Female or Male as the user requests
 #' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
-#' @param n_inc numeric. The age/time-increment used in the discretisation of the continuum.
 #' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
 #'
 #' @return A data frame with focal´s age, related ages, stages, sexes, and types of kin for each time-period
@@ -39,7 +38,6 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                               summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
                                               sex_Focal = "Female",
                                               initial_stage_Focal = NULL,
-                                              n_inc = NULL, ## n_inc is the age-class, time-class increment (e.g., 1year,5year,10year)
                                               output_years){
 
   no_years <- length(U_list_females)
@@ -269,7 +267,6 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                 output_years[1],
                                 na,
                                 ns,
-                                n_inc,
                                 output_kin)}
   else{
     kin_out <- create_full_dists_df(relative_data,
@@ -278,7 +275,6 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                     output_years[1],
                                     na,
                                     ns,
-                                    n_inc,
                                     output_kin)}
 
   return(kin_out)
@@ -698,7 +694,6 @@ all_kin_dy_TV <- function(Uf,
 #' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
 #' @param na numeric. Number of ages.
 #' @param ns numeric. Number of stages.
-#' @param n_inc numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.
 #' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
 #'
 #' @return A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
@@ -709,7 +704,6 @@ create_cumsum_df <- function(kin_matrix_lists,
                              start_year,
                              na,
                              ns,
-                             n_inc,
                              specific_kin){
   df_year_list <- list()
   for(j in years){
@@ -727,8 +721,8 @@ create_cumsum_df <- function(kin_matrix_lists,
       male_kin <- df[ (1+nr/2) : nr, 1:nc]
       female_kin$stage <- rep(seq(1, ns), na)
       male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
-      male_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
       female_kin$Sex <- "Female"
       male_kin$Sex <- "Male"
       both_kin <- rbind(female_kin, male_kin)
@@ -767,7 +761,6 @@ create_cumsum_df <- function(kin_matrix_lists,
 #' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
 #' @param na numeric. Number of ages.
 #' @param ns numeric. Number of stages.
-#' @param n_inc numeric. The size of the age/time increment (if abridged). NULL corresponds to 1 year intervals.
 #' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
 #'
 #' @return A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
@@ -778,7 +771,6 @@ create_full_dists_df <- function(kin_matrix_lists,
                                  start_year,
                                  na,
                                  ns,
-                                 n_inc,
                                  specific_kin){
   df_year_list <- list()
   for(j in years){
@@ -796,8 +788,8 @@ create_full_dists_df <- function(kin_matrix_lists,
       male_kin <- df[ (1+nr/2) : nr, 1:nc]
       female_kin$stage <- rep(seq(1, ns), na)
       male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
-      male_kin$age <- rep(seq(0, (na-1), n_inc), each = ns)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
       female_kin$Sex <- "Female"
       male_kin$Sex <- "Male"
       both_kin <- rbind(female_kin, male_kin)
diff --git a/man/kin_multi_stage_time_variant_2sex.Rd b/man/kin_multi_stage_time_variant_2sex.Rd
index 8eadc67..d6938ef 100644
--- a/man/kin_multi_stage_time_variant_2sex.Rd
+++ b/man/kin_multi_stage_time_variant_2sex.Rd
@@ -18,7 +18,6 @@ kin_multi_stage_time_variant_2sex(
   summary_kin = TRUE,
   sex_Focal = "Female",
   initial_stage_Focal = NULL,
-  n_inc = NULL,
   output_years
 )
 }
@@ -51,8 +50,6 @@ a list of stochastic matrices which describe age-specific male probabilities of
 
 \item{initial_stage_Focal}{Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)}
 
-\item{n_inc}{numeric. The age/time-increment used in the discretisation of the continuum.}
-
 \item{output_years}{vector. The times at which we wish to count kin: start year = output_years\link{1}, and end year = output_years\link{length.}}
 }
 \value{
diff --git a/tests/testthat/test-kin_twosex_multistate_timevariant.R b/tests/testthat/test-kin_twosex_multistate_timevariant.R
index d89b8e7..d6fca9d 100644
--- a/tests/testthat/test-kin_twosex_multistate_timevariant.R
+++ b/tests/testthat/test-kin_twosex_multistate_timevariant.R
@@ -33,7 +33,6 @@ test_that("same output in multi_stage (caswell 2020)", {
                                                   summary_kin = FALSE,
                                                   sex_Focal = "Female", ##  define Focal's sex at birth
                                                   initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                                  n_inc = 1, # width of age class
                                                   seq(1990, (1990)))
 
 
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 239ba5c..7b95acc 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -114,7 +114,6 @@ kin_out_1965_2005 <-
                                     summary_kin = TRUE,
                                     sex_Focal = "Female", ##  define Focal's sex at birth
                                     initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    n_inc = 1, # width of age class
                                     output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
   )
 ```
@@ -193,9 +192,8 @@ kin_out_1965_2005_full <-
                                     output_kin = FALSE,
                                     summary_kin = FALSE,
                                     sex_Focal = "Female", ##  define Focal's sex at birth
-initial_stage_Focal = 1, ## Define Focal's stage at birth
-n_inc = 1, # width of age class
-output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
+                                    initial_stage_Focal = 1, ## Define Focal's stage at birth
+                                    output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
 )
 ```
 ### 2.1. Visualizing kin ###

From fef124883d20ed5841224f749c8d3dcb098d1e48 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 16:24:03 +0100
Subject: [PATCH 42/89] re-naming data (to show we use list)

---
 ...e_UK.Rds => Parity_transfers_by_age_list_UK.Rds} | Bin
 .../Reference_TwoSex_MultiState_TimeVariant.Rmd     |  12 +++++++++---
 2 files changed, 9 insertions(+), 3 deletions(-)
 rename data/{Parity_transfers_by_age_UK.Rds => Parity_transfers_by_age_list_UK.Rds} (100%)

diff --git a/data/Parity_transfers_by_age_UK.Rds b/data/Parity_transfers_by_age_list_UK.Rds
similarity index 100%
rename from data/Parity_transfers_by_age_UK.Rds
rename to data/Parity_transfers_by_age_list_UK.Rds
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 7b95acc..72aea83 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -9,7 +9,13 @@ vignette: >
 %\VignetteEncoding{UTF-8}
 ---
 
-  Note that this is currently an R project and not package. To run code in this vignette we need to run/source all files in the folder ``Matrix Model''.
+
+```{r, eval = T, include=FALSE}
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+library(devtools); load_all()
+```
+
+
 
 ```{R}
 library(DemoKin)
@@ -61,8 +67,8 @@ To avoid further calculations these lists are constructed in another file and si
 
 F_mat_fem <- readr::read_rds(here::here("data","Female_parity_fert_list_UK.Rds"))
 F_mat_male <- readr::read_rds(here::here("data","Male_parity_fert_list_UK.Rds"))
-T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_UK.Rds"))
-T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_UK.Rds"))
+T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
+T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
 U_mat_fem <- readr::read_rds(here::here("data","Female_parity_mortality_list_UK.Rds"))
 U_mat_male <- readr::read_rds(here::here("data","Male_parity_mortality_list_UK.Rds"))
 H_mat <- readr::read_rds(here::here("data","Redistribution_by_parity_list_UK.Rds"))

From 9c71361374aa111fe9caa7d3f2e3dd02a860103d Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 16:35:13 +0100
Subject: [PATCH 43/89] updating vignette

---
 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 72aea83..86e77a0 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -90,13 +90,12 @@ T_mat_male == T_mat_fem.
 H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
 
 We feed these inputs into the matrix model, along with other arguments:
-UK sex ratio --> alpha = 0.51
-We want all of Focal's kin network --> specific_kin = F
-We are considering parity --> parity = T
+UK sex ratio --> birth_female = 0.49
+We are considering parity --> parity = TRUE
+We want all of Focal's kin network --> output_kin = FALSE
 Focal is female --> sex_Focal = "Female"
-Accumulated kin in this example --> dist_output = FALSE
-Focal born into parity 0 --> stage_Focal = 1
-age-increments by 1 year --> nc = 1
+Accumulated kin in this example --> summary_kin = FALSE
+Focal born into parity 0 --> initial_stage_Focal = 1
 timescale from 1965-1985 -- > seq(1965, 1965 + 40)
 
 ### 1. For now lets simply consider the accumulated number of kin Focal expects over the lifecourse ###

From 95607fdc98a02a7f0dfe532540c381373e00eb81 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 16:36:38 +0100
Subject: [PATCH 44/89] updating vignette

---
 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 86e77a0..3e538f2 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -93,8 +93,8 @@ We feed these inputs into the matrix model, along with other arguments:
 UK sex ratio --> birth_female = 0.49
 We are considering parity --> parity = TRUE
 We want all of Focal's kin network --> output_kin = FALSE
+Accumulated kin in this example --> summary_kin = TRUE
 Focal is female --> sex_Focal = "Female"
-Accumulated kin in this example --> summary_kin = FALSE
 Focal born into parity 0 --> initial_stage_Focal = 1
 timescale from 1965-1985 -- > seq(1965, 1965 + 40)
 
@@ -177,7 +177,7 @@ the RHS plot (1965 cohort) simply reflects the fact that Focal will not start re
 
 ### 2. Now lets consider the distributions of kin Focal expects over the lifecourse ###
 
-To obtain distributions of kin as output, we simply change the function argument: dist_output = TRUE
+To obtain distributions of kin as output, we simply change the function argument: summary_kin = FALSE
 
 ```{r, message=FALSE, warning=FALSE}
 rm(kin_out_1965_2005)

From 1bc314d37231cc34ba2810d9dccb4a923943656c Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 16:44:38 +0100
Subject: [PATCH 45/89] testthat file -- we don't need to pre-load data now

---
 .../test-kin_twosex_multistate_timevariant.R  | 22 +++++++++----------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/tests/testthat/test-kin_twosex_multistate_timevariant.R b/tests/testthat/test-kin_twosex_multistate_timevariant.R
index d6fca9d..f04f42a 100644
--- a/tests/testthat/test-kin_twosex_multistate_timevariant.R
+++ b/tests/testthat/test-kin_twosex_multistate_timevariant.R
@@ -3,22 +3,20 @@
 
 
 
+
+
 test_that("same output in multi_stage (caswell 2020)", {
-  load("data/svk_Uxs.rda")
-  Tf <- unname(svk_Uxs)
-  Tm <- unname(svk_Uxs)
-  load("data/svk_fxs.rda")
-  Ff <- unname(svk_fxs)
-  Fm <- unname(svk_fxs)
+  Tf <- svk_Uxs
+  Tm <- svk_Uxs
+  Ff <- svk_fxs
+  Fm <- svk_fxs
   Ff <- (1/0.49)*Ff
   Fm <- (1/0.49)*Fm
-  load("data/svk_pxs.rda")
-  Uf <- unname(svk_pxs)
-  Um <- unname(svk_pxs)
-  load("data/svk_Hxs.rda")
-  H <- unname(svk_Hxs)
+  Uf <- svk_pxs
+  Um <- svk_pxs
+  H <- svk_Hxs
+
 
-  load("data/kin_svk1990_caswell2020.rda")
 
   joe_output <- kin_multi_stage_time_variant_2sex(list(Uf),
                                                   list(Um),

From 4abf7e4640247db3753ff7f8aa3d2437da6bab9b Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Thu, 24 Oct 2024 19:18:03 +0100
Subject: [PATCH 46/89] updating vignette

---
 R/kin_multi_stage_time_variant_2sex.R         | 12 +++----
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 31 ++++++++++++-------
 2 files changed, 26 insertions(+), 17 deletions(-)

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index f3e3d6c..7b8c3e5 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -13,15 +13,15 @@
 #' @param T_list_males list of lists with matrix entries: each outer list entry is period-specific, and composed of
 #'                     a list of stochastic matrices which describe age-specific male probabilities of transferring stage
 #' @param H_list list with matrix entries: redistribution of newborns across each stage to a specific age-class
-#' @param birth_female numeric. ratio of males to females in population
+#' @param birth_female numeric. birth ratio of females to males in population
 #' @param parity logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default `TRUE`.
 #' @param output_kin vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.
-#' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if FALSE, and kin by their age*stage distribution by age of Focal if TRUE.
-#' @param sex_Focal character. Female or Male as the user requests
+#' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if TRUE, and kin by their age*stage distribution by age of Focal if FALSE.
+#' @param sex_Focal character. Female or Male as the user requests.
 #' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
 #' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
 #'
-#' @return A data frame with focal´s age, related ages, stages, sexes, and types of kin for each time-period
+#' @return A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
 
 #' @export
 #'
@@ -34,9 +34,9 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                               H_list = NULL,
                                               birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
                                               parity = FALSE,
-                                              output_kin = FALSE,
+                                              output_kin = FALSE, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
                                               summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
-                                              sex_Focal = "Female",
+                                              sex_Focal = "Female", # Female Focal is default
                                               initial_stage_Focal = NULL,
                                               output_years){
 
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 3e538f2..2cb29b2 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -15,6 +15,7 @@ knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 library(devtools); load_all()
 ```
 
+In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks encompassing both sexes for an average member of a population, the sex of whom is user specified, and who is subject to time-varying demographic rates. We call this individual Focal. We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
 
 
 ```{R}
@@ -28,17 +29,15 @@ options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise
 
 ```
 
-In this vignette, we'll demonstrate how `Two_Sex_Time_Varying_MultiState_Kinship` can be used to compute parity-specific kinship networks for an average member of a population, the sex of whom is user specified, and who is subject to time-varying demographic rates. We call this individual Focal. We seek the number, age, and parity distribution of Focal's relatives for each age of Focal's life as a function of the year in which Focal is born.
+### Kin counts by parity  ###
 
-### Kin counts by parity in a two sex time variant demography ###
-
-In this example we use data for the UK ranging from 1965 - 2005, sourced from the [Human Mortality Database](https://www.mortality.org/) and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). Some simplifying assumptions we make due to data availability are as follows:
+In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from the [Human Mortality Database](https://www.mortality.org/) and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). Some simplifying assumptions we make due to data availability are as follows:
 
 i) Fertility rates vary with time, are distinct among parity class, but the same over sexes (the so-called ``androgynous approximation'').
-ii) Mortality rates vary with time and are distinct across sex, but are the same over parity classes (no parity-specific mortality)
+ii) Mortality rates vary with time, are distinct across sex, but are the same over parity classes (no parity-specific mortality)
 iii) The age-specific probabilities of parity-progression vary with time, but are the same over sex (androgynous approximation again)
 
-In order to implement the model, the function `kin_multi_stage_TV_2_sex` expects the following 7 inputs, fed in as lists:
+In order to implement the model, the function `kin_multi_stage_time_variant_2sex` expects the following 7 inputs of vital rates, fed in as lists:
 
 1) LIST: Female age-and-parity specific survival probabilities over the timescale.
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
@@ -59,7 +58,7 @@ The outer list has length = the timescale. The inner list has length = number of
 
 7) LIST: Length = timescale, and each element is a matrix which assigns the offspring of individuals in some stage to the appropriate age class (age in rows and states in columns)
 
-To avoid further calculations these lists are constructed in another file and simply imported below (See ``Examples'' , ``Matrix_construction'' for details). The code below reads in the above function input lists.
+To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed in another file and simply imported below. The code below reads in the above function input lists.
 
 ```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
 
@@ -89,16 +88,25 @@ Outer list starting 1965 ending 2022
 T_mat_male == T_mat_fem.
 H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
 
-We feed these inputs into the matrix model, along with other arguments:
+
+
+### 1. Accumulated number of kin Focal expects over the lifecourse under time-varying rates from 1965 to 2005 ###
+
+We feed the above inputs into the matrix model, along with other arguments:
 UK sex ratio --> birth_female = 0.49
 We are considering parity --> parity = TRUE
 We want all of Focal's kin network --> output_kin = FALSE
 Accumulated kin in this example --> summary_kin = TRUE
 Focal is female --> sex_Focal = "Female"
 Focal born into parity 0 --> initial_stage_Focal = 1
-timescale from 1965-1985 -- > seq(1965, 1965 + 40)
+timescale from 1965-1985 -- > output_years = seq(1965, 1965 + 40)
+
+Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage 
+distribution of kin. 
 
-### 1. For now lets simply consider the accumulated number of kin Focal expects over the lifecourse ###
+Notice that the timescale argument `output_years` = seq(1965,2005) gives a sequence of 1965,1966,...,2004,2005 of length 41. The first sets of time-varying vital rates
+in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965), the 41-st entry is U_mat_fem[[(1+40)]] (mortality in 2005). We require consistency between
+the length of the list of vital rates and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005)
 
 ```{r, message=FALSE, warning=FALSE}
 # Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
@@ -126,7 +134,8 @@ kin_out_1965_2005 <-
 
 ### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
 
-For an example, let's say we really want to understand the age*parity distributions of the accumulated number of aunts and uncles older than Focal's mother and father, for each age of Focal, and for each period in time from 1965-1985. Some people will do....
+Let's suppose that we really want to understand the age*parity distributions of the accumulated number of aunts and uncles older than Focal's mother and father, for each age of Focal. Some people will do.... Here we look at snapshots in time of the years 1965,1975,1985,1995,2005 and plot the expected age-parity distribution of an average aged
+Focal. Implicit in the below plot is that we really plot Focal's born into different cohorts -- i.e., in the 2005 plot a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
 kin_out_1965_2005 %>%

From 74039f5f7256a8707d971e340ae6749c385998e8 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 13:41:01 +0100
Subject: [PATCH 47/89] Working on vignette

---
 .../test-kin_twosex_multistate_timevariant.R  | 24 ++++++++++---------
 ...eference_TwoSex_MultiState_TimeVariant.Rmd |  2 ++
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/tests/testthat/test-kin_twosex_multistate_timevariant.R b/tests/testthat/test-kin_twosex_multistate_timevariant.R
index f04f42a..1dcea27 100644
--- a/tests/testthat/test-kin_twosex_multistate_timevariant.R
+++ b/tests/testthat/test-kin_twosex_multistate_timevariant.R
@@ -1,8 +1,14 @@
 
+# Here I test the two-sex time-variant multi-stage function against caswell 2020. To do so I restrict the input years
+# to only 1990 (so the model becomes invariant, and restrict the output results to females only)
 
+# Technical note: to ensure that the two sex c(pi_f, pi_m) has a pi_f equal to the pi used in Caswell 2020 we need to guarantee
+# that the spectral radius of the block structured A = U_proj + F_star comes from the upper-left block:
 
-
-
+# A = [U_fem , 0 ; 0 , U_male] + [(1-alpha)*F_fem , 0 ; alpha*F_male , 0].
+# If lambda spectral radius of A[1:n,1:n] then lambda*w[1:n] = A[1,1] %*% w[1:n] = (U_fem + (1-alpha)*F_fem) %*% w[1:n] then w[1:n]*(1/(1-alpha)) %*% F_fem[1,] is the same pi used in Caswell.
+# However, if lambda comes from bottom right block of A then w[1:n]*F_fem[1,] is not the same as pi in Caswell 2020.
+# Numerically check!
 
 
 test_that("same output in multi_stage (caswell 2020)", {
@@ -16,8 +22,6 @@ test_that("same output in multi_stage (caswell 2020)", {
   Um <- svk_pxs
   H <- svk_Hxs
 
-
-
   joe_output <- kin_multi_stage_time_variant_2sex(list(Uf),
                                                   list(Um),
                                                   list(Ff),
@@ -37,8 +41,8 @@ test_that("same output in multi_stage (caswell 2020)", {
   ## Younger sisters
   jcmp_ys <- joe_output %>% dplyr::filter(sex_kin == "Female", group == "ys") %>%
     dplyr::select(age_focal, age_kin, stage_kin, count) %>%
-    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count)
-  hals_output_ys <- kin_svk1990_caswell2020$ys
+    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count) # Joe's
+  hals_output_ys <- kin_svk1990_caswell2020$ys # Hal's
   hals_output_ys <- as.data.frame(hals_output_ys)
   colnames(hals_output_ys) <- seq(0,109,1)
   hals_output_ys$age_kin <- rep(seq(0, (110-1), 1), each = 6)
@@ -52,8 +56,8 @@ test_that("same output in multi_stage (caswell 2020)", {
   ## Older sisters
   jcmp_os <- joe_output %>% dplyr::filter(sex_kin == "Female", group == "os") %>%
     dplyr::select(age_focal, age_kin, stage_kin, count) %>%
-    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count)
-  hals_output_os <- kin_svk1990_caswell2020$os
+    dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count) # Joe's
+  hals_output_os <- kin_svk1990_caswell2020$os # Hal's
   hals_output_os <- as.data.frame(hals_output_os)
   colnames(hals_output_os) <- seq(0,109,1)
   hals_output_os$age_kin <- rep(seq(0, (110-1), 1), each = 6)
@@ -64,9 +68,7 @@ test_that("same output in multi_stage (caswell 2020)", {
     dplyr::select(age_kin, stage_kin, age_focal, count) %>%
     dplyr::transmute(age_focal = age_focal, age_kin = age_kin, stage_kin = stage_kin, count = count)
 
-
-
-
+  ## Check equivalence
   expect_equal(jcmp_ys$count, hcmp_ys$count)
   expect_equal(jcmp_os$count, hcmp_os$count)
 
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 2cb29b2..d556522 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -15,6 +15,8 @@ knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 library(devtools); load_all()
 ```
 
+Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model of kinship, there have been many extensions to the framework (many of which are documented within this package). Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates, Caswell [-@caswell_formal_2022] introduced two-sexes to the model, and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin. Here, we provide an R function which combines the three aforementioned models. 
+
 In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks encompassing both sexes for an average member of a population, the sex of whom is user specified, and who is subject to time-varying demographic rates. We call this individual Focal. We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
 
 

From dce01e5c80d45988b3bfb48a311ca273c6343225 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 13:44:11 +0100
Subject: [PATCH 48/89] vignette

---
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 39 +++++++++++++------
 1 file changed, 28 insertions(+), 11 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index d556522..52e4eda 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -9,15 +9,22 @@ vignette: >
 %\VignetteEncoding{UTF-8}
 ---
 
-
 ```{r, eval = T, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 library(devtools); load_all()
 ```
 
-Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model of kinship, there have been many extensions to the framework (many of which are documented within this package). Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates, Caswell [-@caswell_formal_2022] introduced two-sexes to the model, and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin. Here, we provide an R function which combines the three aforementioned models. 
+Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model 
+of kinship, there have been many extensions to the framework (many of which are documented within this package). 
+Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates, 
+Caswell [-@caswell_formal_2022] introduced two-sexes to the model, 
+and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin. 
+Here, we provide an R function which combines the three aforementioned models. 
 
-In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks encompassing both sexes for an average member of a population, the sex of whom is user specified, and who is subject to time-varying demographic rates. We call this individual Focal. We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
+In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks 
+encompassing both sexes for an average member of a population, the sex of whom is user specified, 
+and who is subject to time-varying demographic rates. We call this individual Focal. 
+We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
 
 
 ```{R}
@@ -30,10 +37,11 @@ library(tictoc)
 options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise output (but also #lose #progress bar)
 
 ```
-
 ### Kin counts by parity  ###
 
-In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from the [Human Mortality Database](https://www.mortality.org/) and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). Some simplifying assumptions we make due to data availability are as follows:
+In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from 
+the [Human Mortality Database](https://www.mortality.org/) and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). 
+Some simplifying assumptions we make due to data availability are as follows:
 
 i) Fertility rates vary with time, are distinct among parity class, but the same over sexes (the so-called ``androgynous approximation'').
 ii) Mortality rates vary with time, are distinct across sex, but are the same over parity classes (no parity-specific mortality)
@@ -54,13 +62,17 @@ This input list has length = the timescale, and each entry represents the rates
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
 
 5) OUTER LIST with INNER LISTS: Female age-specific probabilities of moving up parity over the timescale.
-The outer list has length = the timescale. The inner list has length = number of ages. Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage. Thus for each year, we have a list of age-specific probabilities of moving from one stage to the next.
+The outer list has length = the timescale. The inner list has length = number of ages. 
+Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage. 
+Thus for each year, we have a list of age-specific probabilities of moving from one stage to the next.
 
 6) Same as 5) but for males
 
-7) LIST: Length = timescale, and each element is a matrix which assigns the offspring of individuals in some stage to the appropriate age class (age in rows and states in columns)
+7) LIST: Length = timescale, and each element is a matrix which assigns the offspring of individuals in some stage to 
+the appropriate age class (age in rows and states in columns)
 
-To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed in another file and simply imported below. The code below reads in the above function input lists.
+To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed 
+in another file and simply imported below. The code below reads in the above function input lists.
 
 ```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
 
@@ -80,17 +92,22 @@ Recap: above are lists of period-specific demographic rates, in particular compr
 
 U_mat_fem: list of age by stage matrices, entries give female probability of survival.
 List starting 1965 ending 2022.
+
 U_mat_male: list of age by stage matrices, entries give female probability of survival.
 List starting 1965 ending 2022.
+
 F_mat_fem: list of age by stage matrices, entries give female fert,
 List starting 1965 ending 2022.
+
 F_mat_male == F_mat_fem.
-T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities a female moves up parity (inner list has length of number of age-classes).
+
+T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities 
+a female moves up parity (inner list has length of number of age-classes).
 Outer list starting 1965 ending 2022
-T_mat_male == T_mat_fem.
-H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
 
+T_mat_male == T_mat_fem.
 
+H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
 
 ### 1. Accumulated number of kin Focal expects over the lifecourse under time-varying rates from 1965 to 2005 ###
 

From 48eeaa2160f22bd50eb88a602f3e065f02beadf4 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 13:48:04 +0100
Subject: [PATCH 49/89] vignette

---
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 43 ++++++++++++-------
 1 file changed, 27 insertions(+), 16 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 52e4eda..b951e3b 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -40,7 +40,8 @@ options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise
 ### Kin counts by parity  ###
 
 In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from 
-the [Human Mortality Database](https://www.mortality.org/) and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). 
+the [Human Mortality Database](https://www.mortality.org/) 
+and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). 
 Some simplifying assumptions we make due to data availability are as follows:
 
 i) Fertility rates vary with time, are distinct among parity class, but the same over sexes (the so-called ``androgynous approximation'').
@@ -112,13 +113,13 @@ H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0
 ### 1. Accumulated number of kin Focal expects over the lifecourse under time-varying rates from 1965 to 2005 ###
 
 We feed the above inputs into the matrix model, along with other arguments:
-UK sex ratio --> birth_female = 0.49
-We are considering parity --> parity = TRUE
-We want all of Focal's kin network --> output_kin = FALSE
-Accumulated kin in this example --> summary_kin = TRUE
-Focal is female --> sex_Focal = "Female"
-Focal born into parity 0 --> initial_stage_Focal = 1
-timescale from 1965-1985 -- > output_years = seq(1965, 1965 + 40)
+UK sex ratio --> `birth_female` = 0.49
+We are considering parity --> `parity` = TRUE
+We want all of Focal's kin network --> `output_kin` = FALSE
+Accumulated kin in this example --> `summary_kin` = TRUE
+Focal is female --> `sex_Focal` = "Female"
+Focal born into parity 0 --> `initial_stage_Focal` = 1
+timescale from 1965-1985 -- > `output_years` = seq(1965, 1965 + 40)
 
 Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage 
 distribution of kin. 
@@ -153,8 +154,11 @@ kin_out_1965_2005 <-
 
 ### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
 
-Let's suppose that we really want to understand the age*parity distributions of the accumulated number of aunts and uncles older than Focal's mother and father, for each age of Focal. Some people will do.... Here we look at snapshots in time of the years 1965,1975,1985,1995,2005 and plot the expected age-parity distribution of an average aged
-Focal. Implicit in the below plot is that we really plot Focal's born into different cohorts -- i.e., in the 2005 plot a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
+Let's suppose that we really want to understand the age*parity distributions of the accumulated number 
+of aunts and uncles older than Focal's mother and father, for each age of Focal. Some people will do.... 
+Here we look at snapshots in time of the years 1965,1975,1985,1995,2005 and plot the expected age-parity distribution of an average aged
+Focal. Implicit in the below plot is that we really plot Focal's born into different cohorts -- i.e., in the 2005 
+plot a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
 kin_out_1965_2005 %>%
@@ -184,8 +188,8 @@ kin_out_1965_2005 %>%
 ```
 ### 1.1.2. Plotting the kin of Focal as a function of Focal's cohort of birth ####
 
-Since we only ran the model for 40 years (between 1968-2005), there is very little scope to view kinship as cohort-specific. We can however compare cohorts for 40-year segments of Focal's life.
-Below, following from the above example, we once again consider offspring
+Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific. 
+We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring
 
 ```{r, fig.height = 6, fig.width = 8}
 kin_out_1965_2005 %>%
@@ -197,9 +201,14 @@ kin_out_1965_2005 %>%
   ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
   ggplot2::ylab("Offspring")
 ```
-The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
+The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. 
+Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. 
+The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
 
-The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in partity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by a well mixed parity-distribution at this age of Focal.
+The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. 
+However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. 
+Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by 
+a well mixed parity-distribution at this age of Focal.
 
 the RHS plot (1965 cohort) simply reflects the fact that Focal will not start reproduction until around 15 years old.
 
@@ -234,7 +243,8 @@ kin_out_1965_2005_full <-
 Let us now visualize the distribution of relatives over Focal's lifecourse using the model output
 ### 2.1.1. Plotting kin distributions for an average Focal of fixed age, at some fixed period in time ###
 
-Below I plot the expected age*stage distribution of an average Focal's younger siblings over the years 1965, 1975, 1085, 1995, and 2005, given Focal is of age 50
+Below I plot the expected age*stage distribution of an average Focal's younger siblings over the 
+years 1965, 1975, 1085, 1995, and 2005, given Focal is of age 50
 
 ```{r, fig.height = 6, fig.width = 8}
 kin_out_1965_2005_full %>%
@@ -250,7 +260,8 @@ kin_out_1965_2005_full %>%
   ggplot2::ylab("Younger siblings") +
   ggplot2::ggtitle("Focal 50")
 ```
-As expected, the discontinuity reflects the fact that Focal's younger siblings cannot be of age >=50. Contrastingly, when we look at the age*stage distribution of older siblings, the discontinuity bounds kin to be of age >50, as plotted below
+As expected, the discontinuity reflects the fact that Focal's younger siblings cannot be of age >=50. 
+Contrastingly, when we look at the age*stage distribution of older siblings, the discontinuity bounds kin to be of age >50, as plotted below
 
 ```{r, fig.height = 6, fig.width = 8}
 kin_out_1965_2005_full %>%

From 02f702d160bef4c091fa6be732dc904e774ab4a5 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 13:54:32 +0100
Subject: [PATCH 50/89] vignette

---
 ...Reference_TwoSex_MultiState_TimeVariant.Rmd | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index b951e3b..46e5c1f 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -150,15 +150,25 @@ kin_out_1965_2005 <-
                                     output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
   )
 ```
-### 1.1. Visualizing kin ###
+### 1.1. Visualizing the output ###
+
+```{r, message=FALSE, warning=FALSE}
+
+head(kin_out_1965_2005)
+```
+
+Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin, 
+and produce the marginal stage distribution given age of Focal. We have a column corresponding to sex of kin `sex_kin`,
+a column showing which `year` we are considering, and a column showing Focal's cohort of birth `cohort` (e.g., year - age of Focal).
+
 
 ### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
 
 Let's suppose that we really want to understand the age*parity distributions of the accumulated number 
 of aunts and uncles older than Focal's mother and father, for each age of Focal. Some people will do.... 
-Here we look at snapshots in time of the years 1965,1975,1985,1995,2005 and plot the expected age-parity distribution of an average aged
-Focal. Implicit in the below plot is that we really plot Focal's born into different cohorts -- i.e., in the 2005 
-plot a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
+Here we look at snapshots in time of the years 1965, 1975, 1985, 1995, 2005 and plot the expected age-parity 
+distribution of an average aged Focal. Implicit in the below plot is that we really plot Focal's born into different 
+cohorts -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
 kin_out_1965_2005 %>%

From 16aeb2578bea0e43ead5083669dd54714f7e6204 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 13:58:48 +0100
Subject: [PATCH 51/89] vignette

---
 .../Reference_TwoSex_MultiState_TimeVariant.Rmd | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 46e5c1f..cb1dbe3 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -248,9 +248,17 @@ kin_out_1965_2005_full <-
                                     output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
 )
 ```
-### 2.1. Visualizing kin ###
+### 2.1. Visualizing the output ###
+
+```{r, message=FALSE, warning=FALSE}
+
+head(kin_out_1965_2005_full)
+```
+
+Notice the additional column `age_kin`. Rather than grouping kin by stage and summing over all ages,
+the output here (in data frame form) gives an expected number of kin for each age*stage combination, for each age of Focal.
+
 
-Let us now visualize the distribution of relatives over Focal's lifecourse using the model output
 ### 2.1.1. Plotting kin distributions for an average Focal of fixed age, at some fixed period in time ###
 
 Below I plot the expected age*stage distribution of an average Focal's younger siblings over the 
@@ -270,8 +278,8 @@ kin_out_1965_2005_full %>%
   ggplot2::ylab("Younger siblings") +
   ggplot2::ggtitle("Focal 50")
 ```
-As expected, the discontinuity reflects the fact that Focal's younger siblings cannot be of age >=50. 
-Contrastingly, when we look at the age*stage distribution of older siblings, the discontinuity bounds kin to be of age >50, as plotted below
+
+The discontinuity above reflects the fact that Focal's younger siblings cannot be of age >=50. Contrastingly, when we look at the age*stage distribution of older siblings, the discontinuity bounds kin to be of age >50, as plotted below
 
 ```{r, fig.height = 6, fig.width = 8}
 kin_out_1965_2005_full %>%
@@ -287,6 +295,7 @@ kin_out_1965_2005_full %>%
   ggplot2::ylab("Older siblings") +
   ggplot2::ggtitle("Focal 50")
 ```
+
 With a simple bit of playing with the output data frame, we can plot the age*stage distribution of the combined siblings of Focal
 
 ```{r, fig.height = 6, fig.width = 8}

From 4a3ebf17bbdf1856b2980c1154d354228177b02e Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 14:42:46 +0100
Subject: [PATCH 52/89] vignette

---
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 36 ++++++++++---------
 1 file changed, 19 insertions(+), 17 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index cb1dbe3..40f5c01 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -159,16 +159,19 @@ head(kin_out_1965_2005)
 
 Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin, 
 and produce the marginal stage distribution given age of Focal. We have a column corresponding to sex of kin `sex_kin`,
-a column showing which `year` we are considering, and a column showing Focal's cohort of birth `cohort` (e.g., year - age of Focal).
+a column showing which `year` we are considering, and a column headed `group` which selects the kin type.
+Finally, we have columns showing Focal's cohort of birth `cohort` (e.g., year - age of Focal), and an as.factor() equivalent.
 
 
 ### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
 
 Let's suppose that we really want to understand the age*parity distributions of the accumulated number 
-of aunts and uncles older than Focal's mother and father, for each age of Focal. Some people will do.... 
-Here we look at snapshots in time of the years 1965, 1975, 1985, 1995, 2005 and plot the expected age-parity 
-distribution of an average aged Focal. Implicit in the below plot is that we really plot Focal's born into different 
-cohorts -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
+of aunts and uncles older than Focal's mother and father, for each age of Focal, over years 1965, 1975, 1985, 1995, 2005. 
+Some people will do.... 
+
+We restrict Focal's kinship network to aunts and uncles older than Focal's mother by `group` == "oa". We visualise the marginal
+parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different colour schemes. Implicit in the 
+below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
 kin_out_1965_2005 %>%
@@ -182,7 +185,7 @@ kin_out_1965_2005 %>%
   ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
   ggplot2::ylab("Older aunts and uncles")
 ```
-We could also consider any other kin in Focal's network, for instance, offspring
+We could also consider any other kin in Focal's network, for instance, offspring using `group` == "d"
 
 ```{r, fig.height=6, fig.width=8}
 kin_out_1965_2005 %>%
@@ -199,7 +202,7 @@ kin_out_1965_2005 %>%
 ### 1.1.2. Plotting the kin of Focal as a function of Focal's cohort of birth ####
 
 Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific. 
-We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring
+We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring and only show Focals born of `cohort` 1910, 1925, or 1965:
 
 ```{r, fig.height = 6, fig.width = 8}
 kin_out_1965_2005 %>%
@@ -211,20 +214,16 @@ kin_out_1965_2005 %>%
   ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
   ggplot2::ylab("Offspring")
 ```
-The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. 
-Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. 
-The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
+The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
 
-The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. 
-However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. 
-Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by 
+The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by 
 a well mixed parity-distribution at this age of Focal.
 
 the RHS plot (1965 cohort) simply reflects the fact that Focal will not start reproduction until around 15 years old.
 
 ### 2. Now lets consider the distributions of kin Focal expects over the lifecourse ###
 
-To obtain distributions of kin as output, we simply change the function argument: summary_kin = FALSE
+To obtain distributions of kin as output, we simply change the function argument: `summary_kin` = FALSE
 
 ```{r, message=FALSE, warning=FALSE}
 rm(kin_out_1965_2005)
@@ -261,8 +260,9 @@ the output here (in data frame form) gives an expected number of kin for each ag
 
 ### 2.1.1. Plotting kin distributions for an average Focal of fixed age, at some fixed period in time ###
 
-Below I plot the expected age*stage distribution of an average Focal's younger siblings over the 
-years 1965, 1975, 1085, 1995, and 2005, given Focal is of age 50
+Lets's consider Focal is aged 50 `age_focal` == 50, and examine kin younger siblings; `group` == "ys".
+Restricting ourselves to the years 1965, 1975, 1985, 1995, 2005, we can plot the expected age*stage distribution
+of these kin over the considered periods, as shown below:
 
 ```{r, fig.height = 6, fig.width = 8}
 kin_out_1965_2005_full %>%
@@ -279,7 +279,9 @@ kin_out_1965_2005_full %>%
   ggplot2::ggtitle("Focal 50")
 ```
 
-The discontinuity above reflects the fact that Focal's younger siblings cannot be of age >=50. Contrastingly, when we look at the age*stage distribution of older siblings, the discontinuity bounds kin to be of age >50, as plotted below
+Notice the discontinuity along the x-abissca at 50. This reflects the fact that Focal's younger siblings 
+cannot are of age <50. Contrastingly, when we look at the age*stage distribution of older siblings, we observe another
+discontinuity which bounds kin to be of age >50, as plotted below:
 
 ```{r, fig.height = 6, fig.width = 8}
 kin_out_1965_2005_full %>%

From 590b087c028dd0bdc0ea6d88c934c4a142eaeef5 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 15:16:08 +0100
Subject: [PATCH 53/89] testing rda files

---
 data/Female_parity_fert_list_UK.rda           | Bin 0 -> 62361 bytes
 data/Female_parity_mortality_list_UK.rda      | Bin 0 -> 37402 bytes
 data/Male_parity_fert_list_UK.rda             | Bin 0 -> 62360 bytes
 data/Male_parity_mortality_list_UK.rda        | Bin 0 -> 45435 bytes
 data/Parity_transfers_by_age_list_UK.rda      | Bin 0 -> 136173 bytes
 data/Redistribution_by_parity_list_UK.rda     | Bin 0 -> 338 bytes
 ...eference_TwoSex_MultiState_TimeVariant.Rmd |  62 ++++++++++--------
 7 files changed, 36 insertions(+), 26 deletions(-)
 create mode 100644 data/Female_parity_fert_list_UK.rda
 create mode 100644 data/Female_parity_mortality_list_UK.rda
 create mode 100644 data/Male_parity_fert_list_UK.rda
 create mode 100644 data/Male_parity_mortality_list_UK.rda
 create mode 100644 data/Parity_transfers_by_age_list_UK.rda
 create mode 100644 data/Redistribution_by_parity_list_UK.rda

diff --git a/data/Female_parity_fert_list_UK.rda b/data/Female_parity_fert_list_UK.rda
new file mode 100644
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diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 40f5c01..ecaf57d 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -50,26 +50,26 @@ iii) The age-specific probabilities of parity-progression vary with time, but ar
 
 In order to implement the model, the function `kin_multi_stage_time_variant_2sex` expects the following 7 inputs of vital rates, fed in as lists:
 
-1) LIST: Female age-and-parity specific survival probabilities over the timescale.
+1) `U_list_females` A list of female age-and-parity specific survival probabilities over the timescale (in matrix forms).
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
 
-2) LIST: Male age-and-parity specific survival probabilities over the timescale.
+2) `U_list_males` A list of male age-and-parity specific survival probabilities over the timescale (in matrix forms).
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
 
-3) LIST: Female age-and-parity specific fertility rates over the timescale.
+3) `F_list_females` A list of female age-and-parity specific fertility rates over the timescale (in matrix forms).
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
 
-4) LIST: Male age-and-parity specific fertility rates over the timescale.
+4) `F_list_males` A list of male age-and-parity specific fertility rates over the timescale (in matrix forms).
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
 
-5) OUTER LIST with INNER LISTS: Female age-specific probabilities of moving up parity over the timescale.
+5) `T_list_females` A list of lists of female age-specific probabilities of moving up parity over the timescale (in matrix forms).
 The outer list has length = the timescale. The inner list has length = number of ages. 
 Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage. 
 Thus for each year, we have a list of age-specific probabilities of moving from one stage to the next.
 
 6) Same as 5) but for males
 
-7) LIST: Length = timescale, and each element is a matrix which assigns the offspring of individuals in some stage to 
+7) `H_list` A list of length = timescale, in which each element is a matrix which assigns the offspring of individuals in some stage to 
 the appropriate age class (age in rows and states in columns)
 
 To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed 
@@ -77,15 +77,20 @@ in another file and simply imported below. The code below reads in the above fun
 
 ```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
 
-# Lets construct these lists as model inputs..............
-
-F_mat_fem <- readr::read_rds(here::here("data","Female_parity_fert_list_UK.Rds"))
-F_mat_male <- readr::read_rds(here::here("data","Male_parity_fert_list_UK.Rds"))
-T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
-T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
-U_mat_fem <- readr::read_rds(here::here("data","Female_parity_mortality_list_UK.Rds"))
-U_mat_male <- readr::read_rds(here::here("data","Male_parity_mortality_list_UK.Rds"))
-H_mat <- readr::read_rds(here::here("data","Redistribution_by_parity_list_UK.Rds"))
+F_mat_fem <- Female_parity_fert_list_UK
+F_mat_male <- Male_parity_fert_list_UK
+T_mat_fem <- Parity_transfers_by_age_list_UK
+T_mat_male <- Parity_transfers_by_age_list_UK
+U_mat_fem <- Female_parity_mortality_list_UK
+U_mat_male <- Male_parity_mortality_list_UK
+H_mat <- Redistribution_by_parity_list_UK
+#F_mat_fem <- readr::read_rds(here::here("data","Female_parity_fert_list_UK.Rds"))
+#F_mat_male <- readr::read_rds(here::here("data","Male_parity_fert_list_UK.Rds"))
+#T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
+#T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
+#U_mat_fem <- readr::read_rds(here::here("data","Female_parity_mortality_list_UK.Rds"))
+#U_mat_male <- readr::read_rds(here::here("data","Male_parity_mortality_list_UK.Rds"))
+#H_mat <- readr::read_rds(here::here("data","Redistribution_by_parity_list_UK.Rds"))
 
 ```
 
@@ -124,9 +129,13 @@ timescale from 1965-1985 -- > `output_years` = seq(1965, 1965 + 40)
 Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage 
 distribution of kin. 
 
-Notice that the timescale argument `output_years` = seq(1965,2005) gives a sequence of 1965,1966,...,2004,2005 of length 41. The first sets of time-varying vital rates
-in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965), the 41-st entry is U_mat_fem[[(1+40)]] (mortality in 2005). We require consistency between
-the length of the list of vital rates and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005)
+Notice that the timescale argument `output_years` = seq(1965,2005) gives a sequence of 1965, 1966, ..., 2004, 2005 of length 41. 
+The first sets of time-varying vital rates in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965), 
+the 41-st entry is U_mat_fem[[(1+40)]] (mortality in 2005). We require consistency between the length of the list of vital rates
+and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005). Therefore we use the input lists of demographic rates
+
+`U_list_females` = U_mat_fem[1:(1+no_years)] which runs from U_mat_fem[[1]] = 1965 set of rates, up to U_mat_fem[[41]] = 2005 set of rates, 
+and so on...
 
 ```{r, message=FALSE, warning=FALSE}
 # Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
@@ -134,13 +143,13 @@ no_years <- 40
 # and we start projecting kin in 1965
 # We decide here to count accumulated kin by age of Focal, and not distributions of kin
 kin_out_1965_2005 <-
-  kin_multi_stage_time_variant_2sex(U_mat_fem[1:(1+no_years)],
-                                    U_mat_male[1:(1+no_years)],
-                                    F_mat_fem[1:(1+no_years)],
-                                    F_mat_male[1:(1+no_years)],
-                                    T_mat_fem[1:(1+no_years)],
-                                    T_mat_fem[1:(1+no_years)],
-                                    H_mat[1:(1+no_years)],
+  kin_multi_stage_time_variant_2sex(U_list_females = U_mat_fem[1:(1+no_years)],
+                                    U_list_males = U_mat_male[1:(1+no_years)],
+                                    F_list_females = F_mat_fem[1:(1+no_years)],
+                                    F_list_males = F_mat_male[1:(1+no_years)],
+                                    T_list_females = T_mat_fem[1:(1+no_years)],
+                                    T_list_males = T_mat_fem[1:(1+no_years)],
+                                    H_list = H_mat[1:(1+no_years)],
                                     birth_female = 1 - 0.51, ## Sex ratio -- UK value
                                     parity = TRUE,
                                     output_kin = FALSE,
@@ -171,7 +180,8 @@ Some people will do....
 
 We restrict Focal's kinship network to aunts and uncles older than Focal's mother by `group` == "oa". We visualise the marginal
 parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different colour schemes. Implicit in the 
-below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
+below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, 
+while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
 kin_out_1965_2005 %>%

From 6998f19482b7b77bbcb1e73610e17ee083f665cf Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Fri, 25 Oct 2024 15:19:22 +0100
Subject: [PATCH 54/89] rda

---
 data/Female_parity_fert_list_UK.rda           | Bin 62361 -> 0 bytes
 data/Female_parity_mortality_list_UK.rda      | Bin 37402 -> 0 bytes
 data/Male_parity_fert_list_UK.rda             | Bin 62360 -> 0 bytes
 data/Male_parity_mortality_list_UK.rda        | Bin 45435 -> 0 bytes
 data/Parity_transfers_by_age_list_UK.rda      | Bin 136173 -> 0 bytes
 data/Redistribution_by_parity_list_UK.rda     | Bin 338 -> 0 bytes
 ...eference_TwoSex_MultiState_TimeVariant.Rmd |  22 +++++++-----------
 7 files changed, 8 insertions(+), 14 deletions(-)
 delete mode 100644 data/Female_parity_fert_list_UK.rda
 delete mode 100644 data/Female_parity_mortality_list_UK.rda
 delete mode 100644 data/Male_parity_fert_list_UK.rda
 delete mode 100644 data/Male_parity_mortality_list_UK.rda
 delete mode 100644 data/Parity_transfers_by_age_list_UK.rda
 delete mode 100644 data/Redistribution_by_parity_list_UK.rda

diff --git a/data/Female_parity_fert_list_UK.rda b/data/Female_parity_fert_list_UK.rda
deleted file mode 100644
index 9feb9d5e86f51a49d6728f979f484bf0c7b95901..0000000000000000000000000000000000000000
GIT binary patch
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HcmV?d00001

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diff --git a/data/Male_parity_mortality_list_UK.rda b/data/Male_parity_mortality_list_UK.rda
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diff --git a/data/Redistribution_by_parity_list_UK.rda b/data/Redistribution_by_parity_list_UK.rda
deleted file mode 100644
index 1ada6a344456c412409c48be39c56bf64bbc7580..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 338
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dUDL)cSMi{cI>ht($}P1_@3^LzwmL8{001a4qvQYp

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index ecaf57d..aee92be 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -77,20 +77,14 @@ in another file and simply imported below. The code below reads in the above fun
 
 ```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
 
-F_mat_fem <- Female_parity_fert_list_UK
-F_mat_male <- Male_parity_fert_list_UK
-T_mat_fem <- Parity_transfers_by_age_list_UK
-T_mat_male <- Parity_transfers_by_age_list_UK
-U_mat_fem <- Female_parity_mortality_list_UK
-U_mat_male <- Male_parity_mortality_list_UK
-H_mat <- Redistribution_by_parity_list_UK
-#F_mat_fem <- readr::read_rds(here::here("data","Female_parity_fert_list_UK.Rds"))
-#F_mat_male <- readr::read_rds(here::here("data","Male_parity_fert_list_UK.Rds"))
-#T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
-#T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
-#U_mat_fem <- readr::read_rds(here::here("data","Female_parity_mortality_list_UK.Rds"))
-#U_mat_male <- readr::read_rds(here::here("data","Male_parity_mortality_list_UK.Rds"))
-#H_mat <- readr::read_rds(here::here("data","Redistribution_by_parity_list_UK.Rds"))
+
+F_mat_fem <- readr::read_rds(here::here("data","Female_parity_fert_list_UK.Rds"))
+F_mat_male <- readr::read_rds(here::here("data","Male_parity_fert_list_UK.Rds"))
+T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
+T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
+U_mat_fem <- readr::read_rds(here::here("data","Female_parity_mortality_list_UK.Rds"))
+U_mat_male <- readr::read_rds(here::here("data","Male_parity_mortality_list_UK.Rds"))
+H_mat <- readr::read_rds(here::here("data","Redistribution_by_parity_list_UK.Rds"))
 
 ```
 

From 04c88a0b15ec797691809d022e57decc7c07b744 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Sat, 26 Oct 2024 10:01:11 +0100
Subject: [PATCH 55/89] Setting data files as rda for pre-loading

---
 data/Female_parity_fert_list_UK.rda            | Bin 0 -> 62377 bytes
 data/Female_parity_mortality_list_UK.rda       | Bin 0 -> 37423 bytes
 data/Male_parity_fert_list_UK.rda              | Bin 0 -> 62376 bytes
 data/Male_parity_mortality_list_UK.rda         | Bin 0 -> 45457 bytes
 data/Parity_transfers_by_age_list_UK.rda       | Bin 0 -> 136196 bytes
 data/Redistribution_by_parity_list_UK.rda      | Bin 0 -> 362 bytes
 ...Reference_TwoSex_MultiState_TimeVariant.Rmd |  17 +++++++++--------
 7 files changed, 9 insertions(+), 8 deletions(-)
 create mode 100644 data/Female_parity_fert_list_UK.rda
 create mode 100644 data/Female_parity_mortality_list_UK.rda
 create mode 100644 data/Male_parity_fert_list_UK.rda
 create mode 100644 data/Male_parity_mortality_list_UK.rda
 create mode 100644 data/Parity_transfers_by_age_list_UK.rda
 create mode 100644 data/Redistribution_by_parity_list_UK.rda

diff --git a/data/Female_parity_fert_list_UK.rda b/data/Female_parity_fert_list_UK.rda
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diff --git a/data/Redistribution_by_parity_list_UK.rda b/data/Redistribution_by_parity_list_UK.rda
new file mode 100644
index 0000000000000000000000000000000000000000..7fc1078b1e0e9e9aa78c2534d918c4439fd581b9
GIT binary patch
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diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index aee92be..1346e1f 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -77,14 +77,15 @@ in another file and simply imported below. The code below reads in the above fun
 
 ```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
 
-
-F_mat_fem <- readr::read_rds(here::here("data","Female_parity_fert_list_UK.Rds"))
-F_mat_male <- readr::read_rds(here::here("data","Male_parity_fert_list_UK.Rds"))
-T_mat_fem <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
-T_mat_male <- readr::read_rds(here::here("data","Parity_transfers_by_age_list_UK.Rds"))
-U_mat_fem <- readr::read_rds(here::here("data","Female_parity_mortality_list_UK.Rds"))
-U_mat_male <- readr::read_rds(here::here("data","Male_parity_mortality_list_UK.Rds"))
-H_mat <- readr::read_rds(here::here("data","Redistribution_by_parity_list_UK.Rds"))
+## files loaded as "rda" extensions (from "data/)
+
+F_mat_fem <- Female_parity_fert_list_UK
+F_mat_male <- Male_parity_fert_list_UK
+T_mat_fem <- Parity_transfers_by_age_list_UK
+T_mat_male <- Parity_transfers_by_age_list_UK
+U_mat_fem <- Female_parity_mortality_list_UK
+U_mat_male <- Male_parity_mortality_list_UK
+H_mat <- Redistribution_by_parity_list_UK
 
 ```
 

From 4dd72294795d0f6d3fd3c5d7ea39504e5c2f5945 Mon Sep 17 00:00:00 2001
From: redshank <jb4u23@soton.ac.uk>
Date: Sat, 26 Oct 2024 10:15:06 +0100
Subject: [PATCH 56/89] removing old RDS extensions

---
 data/Female_parity_fert_list_UK.Rds       | Bin 62341 -> 0 bytes
 data/Female_parity_mortality_list_UK.Rds  | Bin 37381 -> 0 bytes
 data/Male_parity_fert_list_UK.Rds         | Bin 62341 -> 0 bytes
 data/Male_parity_mortality_list_UK.Rds    | Bin 45416 -> 0 bytes
 data/Parity_transfers_by_age_list_UK.Rds  | Bin 136152 -> 0 bytes
 data/Redistribution_by_parity_list_UK.Rds | Bin 321 -> 0 bytes
 6 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 data/Female_parity_fert_list_UK.Rds
 delete mode 100644 data/Female_parity_mortality_list_UK.Rds
 delete mode 100644 data/Male_parity_fert_list_UK.Rds
 delete mode 100644 data/Male_parity_mortality_list_UK.Rds
 delete mode 100644 data/Parity_transfers_by_age_list_UK.Rds
 delete mode 100644 data/Redistribution_by_parity_list_UK.Rds

diff --git a/data/Female_parity_fert_list_UK.Rds b/data/Female_parity_fert_list_UK.Rds
deleted file mode 100644
index 1141b1c364d11f194d807da491591071bff21474..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/data/Redistribution_by_parity_list_UK.Rds b/data/Redistribution_by_parity_list_UK.Rds
deleted file mode 100644
index 8603776b6b3bedca910216f9dfbec80b6bbd139d..0000000000000000000000000000000000000000
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HcmV?d00001

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From 3c59fbd040adcb9891f2005d13202df16d1ba234 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 13 Nov 2024 08:19:08 -0300
Subject: [PATCH 57/89] include multi_stage 2 sex time var

---
 NAMESPACE                                     |  1 +
 man/all_kin_dy.Rd                             | 48 ++++++++++
 man/all_kin_dy_TV.Rd                          | 94 +++++++++++++++++++
 man/create_cumsum_df.Rd                       | 38 ++++++++
 man/create_full_dists_df.Rd                   | 38 ++++++++
 man/kin_multi_stage_time_variant_2sex.Rd      |  8 +-
 man/pi_mix.Rd                                 | 33 +++++++
 man/pi_mix_TV.Rd                              | 31 ++++++
 man/pi_mix_TV_parity.Rd                       | 35 +++++++
 man/pi_mix_parity.Rd                          | 33 +++++++
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 14 +--
 11 files changed, 362 insertions(+), 11 deletions(-)
 create mode 100644 man/all_kin_dy.Rd
 create mode 100644 man/all_kin_dy_TV.Rd
 create mode 100644 man/create_cumsum_df.Rd
 create mode 100644 man/create_full_dists_df.Rd
 create mode 100644 man/pi_mix.Rd
 create mode 100644 man/pi_mix_TV.Rd
 create mode 100644 man/pi_mix_TV_parity.Rd
 create mode 100644 man/pi_mix_parity.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 9a087d8..7fa2b73 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -4,6 +4,7 @@ export("%>%")
 export(kin)
 export(kin2sex)
 export(kin_multi_stage)
+export(kin_multi_stage_time_variant_2sex)
 export(kin_time_invariant)
 export(kin_time_invariant_2sex)
 export(kin_time_invariant_2sex_cod)
diff --git a/man/all_kin_dy.Rd b/man/all_kin_dy.Rd
new file mode 100644
index 0000000..261be62
--- /dev/null
+++ b/man/all_kin_dy.Rd
@@ -0,0 +1,48 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{all_kin_dy}
+\alias{all_kin_dy}
+\title{Title time invariant two-sex multi-state kin projections}
+\usage{
+all_kin_dy(
+  Uf,
+  Um,
+  Ff,
+  Fm,
+  alpha,
+  na,
+  ns,
+  Parity,
+  sex_Focal,
+  Initial_stage_Focal
+)
+}
+\arguments{
+\item{Uf}{matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)}
+
+\item{Um}{matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)}
+
+\item{Ff}{matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage}
+
+\item{Fm}{matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage}
+
+\item{alpha}{scalar. birth ratio (male:female)}
+
+\item{na}{scalar. number of ages.}
+
+\item{ns}{scalar. number of stages.}
+
+\item{Parity}{logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting}
+
+\item{sex_Focal}{logical. Female or Male}
+
+\item{Initial_stage_Focal}{numeric. Any natural number {1,2,3,4,...}}
+}
+\value{
+a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+yielding the age*stage distribution of kin for each age of Focal
+}
+\description{
+Title time invariant two-sex multi-state kin projections
+}
diff --git a/man/all_kin_dy_TV.Rd b/man/all_kin_dy_TV.Rd
new file mode 100644
index 0000000..c390385
--- /dev/null
+++ b/man/all_kin_dy_TV.Rd
@@ -0,0 +1,94 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{all_kin_dy_TV}
+\alias{all_kin_dy_TV}
+\title{Title time-variant two-sex multi-state kin projections}
+\usage{
+all_kin_dy_TV(
+  Uf,
+  Um,
+  Ff,
+  Fm,
+  alpha,
+  na,
+  ns,
+  Parity,
+  sex_Focal,
+  Initial_stage_Focal,
+  previous_kin_Focal,
+  prev_kin_children,
+  prev_kin_grandchildren,
+  prev_kin_greatgrandchildren,
+  prev_kin_parents,
+  prev_kin_grand_parents,
+  prev_kin_great_grand_parents,
+  prev_kin_older_sibs,
+  prev_kin_younger_sibs,
+  prev_kin_older_niece_nephew,
+  prev_kin_younger_niece_nephew,
+  prev_kin_older_aunts_uncles,
+  prev_kin_younger_aunts_uncles,
+  prev_kin_older_cousins,
+  prev_kin_younger_cousins,
+  previous_population_age_stage_structure
+)
+}
+\arguments{
+\item{Uf}{matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)}
+
+\item{Um}{matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)}
+
+\item{Ff}{matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage}
+
+\item{Fm}{matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage}
+
+\item{alpha}{scalar. birth ratio (male:female)}
+
+\item{na}{scalar. number of ages.}
+
+\item{ns}{scalar. number of stages.}
+
+\item{Parity}{logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting}
+
+\item{sex_Focal}{logical. Female or Male}
+
+\item{Initial_stage_Focal}{numeric. Any natural number {1,2,3,4,...}}
+
+\item{previous_kin_Focal}{matrix. last years kinship output.}
+
+\item{prev_kin_children}{matrix. last years kinship output.}
+
+\item{prev_kin_grandchildren}{matrix. last years kinship output.}
+
+\item{prev_kin_greatgrandchildren}{matrix. last years kinship output.}
+
+\item{prev_kin_parents}{matrix. last years kinship output.}
+
+\item{prev_kin_grand_parents}{matrix. last years kinship output.}
+
+\item{prev_kin_older_sibs}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_sibs}{matrix. last years kinship output.}
+
+\item{prev_kin_older_niece_nephew}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_niece_nephew}{matrix. last years kinship output.}
+
+\item{prev_kin_older_aunts_uncles}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_aunts_uncles}{matrix. last years kinship output.}
+
+\item{prev_kin_older_cousins}{matrix. last years kinship output.}
+
+\item{prev_kin_younger_cousins}{matrix. last years kinship output.}
+
+\item{previous_population_age_stage_structure}{vector. The transient "population structure" (age*stage distributed)}
+}
+\value{
+a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+yielding the age*stage distribution of kin for each age of Focal
+}
+\description{
+Title time-variant two-sex multi-state kin projections
+}
diff --git a/man/create_cumsum_df.Rd b/man/create_cumsum_df.Rd
new file mode 100644
index 0000000..28bc914
--- /dev/null
+++ b/man/create_cumsum_df.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{create_cumsum_df}
+\alias{create_cumsum_df}
+\title{Title Accumulated kin by each age of Focal, for each time period, and cohort of birth}
+\usage{
+create_cumsum_df(
+  kin_matrix_lists,
+  kin_names,
+  years,
+  start_year,
+  na,
+  ns,
+  specific_kin
+)
+}
+\arguments{
+\item{kin_matrix_lists}{list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+so list(X_focal) = list(X_focal\link{year1},X_focal\link{year2},...,X_focal\link{yearlast})}
+
+\item{kin_names}{list of characters. Corresponding to above lists: list("F","m",....)}
+
+\item{years}{vector. The timescale on which we implement the kinship model.}
+
+\item{start_year}{. First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)}
+
+\item{na}{numeric. Number of ages.}
+
+\item{ns}{numeric. Number of stages.}
+
+\item{specific_kin}{character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.}
+}
+\value{
+A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
+}
+\description{
+Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
+}
diff --git a/man/create_full_dists_df.Rd b/man/create_full_dists_df.Rd
new file mode 100644
index 0000000..94fdb0e
--- /dev/null
+++ b/man/create_full_dists_df.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{create_full_dists_df}
+\alias{create_full_dists_df}
+\title{Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth}
+\usage{
+create_full_dists_df(
+  kin_matrix_lists,
+  kin_names,
+  years,
+  start_year,
+  na,
+  ns,
+  specific_kin
+)
+}
+\arguments{
+\item{kin_matrix_lists}{list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+so list(X_focal) = list(X_focal\link{year1},X_focal\link{year2},...,X_focal\link{yearlast})}
+
+\item{kin_names}{list of characters. Corresponding to above lists: list("F","m",....)}
+
+\item{years}{vector. The timescale on which we implement the kinship model.}
+
+\item{start_year}{. First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)}
+
+\item{na}{numeric. Number of ages.}
+
+\item{ns}{numeric. Number of stages.}
+
+\item{specific_kin}{character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.}
+}
+\value{
+A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
+}
+\description{
+Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
+}
diff --git a/man/kin_multi_stage_time_variant_2sex.Rd b/man/kin_multi_stage_time_variant_2sex.Rd
index d6938ef..82aed18 100644
--- a/man/kin_multi_stage_time_variant_2sex.Rd
+++ b/man/kin_multi_stage_time_variant_2sex.Rd
@@ -38,22 +38,22 @@ a list of stochastic matrices which describe age-specific male probabilities of
 
 \item{H_list}{list with matrix entries: redistribution of newborns across each stage to a specific age-class}
 
-\item{birth_female}{numeric. ratio of males to females in population}
+\item{birth_female}{numeric. birth ratio of females to males in population}
 
 \item{parity}{logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default \code{TRUE}.}
 
 \item{output_kin}{vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.}
 
-\item{summary_kin}{logical. Results as a data frame of accumulated kin by age of Focal if FALSE, and kin by their age*stage distribution by age of Focal if TRUE.}
+\item{summary_kin}{logical. Results as a data frame of accumulated kin by age of Focal if TRUE, and kin by their age*stage distribution by age of Focal if FALSE.}
 
-\item{sex_Focal}{character. Female or Male as the user requests}
+\item{sex_Focal}{character. Female or Male as the user requests.}
 
 \item{initial_stage_Focal}{Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)}
 
 \item{output_years}{vector. The times at which we wish to count kin: start year = output_years\link{1}, and end year = output_years\link{length.}}
 }
 \value{
-A data frame with focal´s age, related ages, stages, sexes, and types of kin for each time-period
+A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
 }
 \description{
 Implementation of combined formal demographic models: Caswell II,III,IV.
diff --git a/man/pi_mix.Rd b/man/pi_mix.Rd
new file mode 100644
index 0000000..0ed3c83
--- /dev/null
+++ b/man/pi_mix.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix}
+\alias{pi_mix}
+\title{Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case}
+\usage{
+pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
+}
+\arguments{
+\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
+
+\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
+
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
+}
diff --git a/man/pi_mix_TV.Rd b/man/pi_mix_TV.Rd
new file mode 100644
index 0000000..5f39775
--- /dev/null
+++ b/man/pi_mix_TV.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix_TV}
+\alias{pi_mix_TV}
+\title{Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case}
+\usage{
+pi_mix_TV(Ff, Fm, alpha, na, ns, previous_age_stage_dist)
+}
+\arguments{
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+
+\item{previous_age_stage_dist}{vector. Last years population structure (age\emph{stage}sex full distribution)}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
+}
diff --git a/man/pi_mix_TV_parity.Rd b/man/pi_mix_TV_parity.Rd
new file mode 100644
index 0000000..9a95ab8
--- /dev/null
+++ b/man/pi_mix_TV_parity.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix_TV_parity}
+\alias{pi_mix_TV_parity}
+\title{Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case}
+\usage{
+pi_mix_TV_parity(Ff, Fm, alpha, na, ns, previous_age_stage_dist)
+}
+\arguments{
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+
+\item{previous_age_stage_dist}{vector. Last years population structure (age\emph{stage}sex full distribution)}
+
+\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
+
+\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
+}
diff --git a/man/pi_mix_parity.Rd b/man/pi_mix_parity.Rd
new file mode 100644
index 0000000..f3874c8
--- /dev/null
+++ b/man/pi_mix_parity.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/kin_multi_stage_time_variant_2sex.R
+\name{pi_mix_parity}
+\alias{pi_mix_parity}
+\title{Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case}
+\usage{
+pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
+}
+\arguments{
+\item{Uf}{matrix. Block-structured matrix which transfers females over stage and advances their age}
+
+\item{Um}{matrix. Block-structured matrix which transfers males over stage and advances their age}
+
+\item{Ff}{matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage}
+
+\item{Fm}{matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage}
+
+\item{alpha}{scalar. Birth ratio male:female}
+
+\item{na}{scalar. Number of age-classes}
+
+\item{ns}{scalar. Number of stages}
+}
+\value{
+list (of vectors). list[\link{1}] = full age\emph{stage}sex distribution
+list[\link{2}] = female age\emph{stage distribution normalised
+list[\link{3}] = male age}stage distribution normalised
+list[\link{4}] = female marginal age distribution normalised
+list[\link{5}] = male marginal age distribution normalised
+}
+\description{
+Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
+}
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 1346e1f..0e5de95 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -1,12 +1,12 @@
 ---
-  title: "Expected kin counts by type of relative in a two-sex multi-state time-varying framework"
+title: "Expected kin counts by type of relative in a two-sex multi-state time-varying framework"
 output:
   html_document:
-  toc: true
-toc_depth: 1
+    toc: true
+    toc_depth: 1
 vignette: >
   %\VignetteEngine{knitr::rmarkdown}
-%\VignetteEncoding{UTF-8}
+  %\VignetteEncoding{UTF-8}
 ---
 
 ```{r, eval = T, include=FALSE}
@@ -27,8 +27,8 @@ and who is subject to time-varying demographic rates. We call this individual Fo
 We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
 
 
-```{R}
-library(DemoKin)
+```{r}
+# library(DemoKin)
 library(Matrix)
 library(tictoc)
 `%>%` <- magrittr::`%>%`
@@ -75,7 +75,7 @@ the appropriate age class (age in rows and states in columns)
 To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed 
 in another file and simply imported below. The code below reads in the above function input lists.
 
-```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
+```{r eval=TRUE, message=FALSE, warning=FALSE, include=TRUE}
 
 ## files loaded as "rda" extensions (from "data/)
 

From 6025af28e4f38567250afbcb2cb17f6de104a47c Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 8 Jan 2025 16:24:01 -0300
Subject: [PATCH 58/89] clean multi_state_variant + fix issues

---
 DESCRIPTION                                   |  3 +-
 R/data.R                                      | 42 ++++++++-
 R/kin_multi_stage_time_variant_2sex.R         | 40 +++++----
 R/kin_time_invariant_2sex.R                   | 10 ++-
 R/kin_time_variant.R                          | 83 +++++++++---------
 R/kin_time_variant_2sex.R                     | 61 +++++++------
 R/kin_time_variant_2sex_cod.R                 | 60 +++++++------
 README.Rmd                                    |  7 +-
 README.md                                     | 67 +++-----------
 man/Female_parity_fert_list_UK.Rd             | 19 ++++
 man/Female_parity_mortality_list_UK.Rd        | 19 ++++
 man/Male_parity_mortality_list_UK.Rd          | 19 ++++
 man/Parity_transfers_by_age_list_UK.Rd        | 19 ++++
 man/Redistribution_by_parity_list_UK.Rd       | 19 ++++
 man/create_cumsum_df.Rd                       |  2 +-
 man/create_full_dists_df.Rd                   |  2 +-
 man/kin_multi_stage_time_variant_2sex.Rd      |  2 +-
 man/timevarying_kin_2sex_cod.Rd               |  4 +-
 .../test-kin_twosex_multistate_timevariant.R  |  4 +-
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 87 ++++++-------------
 20 files changed, 324 insertions(+), 245 deletions(-)
 create mode 100644 man/Female_parity_fert_list_UK.Rd
 create mode 100644 man/Female_parity_mortality_list_UK.Rd
 create mode 100644 man/Male_parity_mortality_list_UK.Rd
 create mode 100644 man/Parity_transfers_by_age_list_UK.Rd
 create mode 100644 man/Redistribution_by_parity_list_UK.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 9dd070e..07aa5d8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -33,7 +33,8 @@ Imports:
     magrittr,
     data.table,
     lifecycle,
-    tictoc
+    tictoc,
+    reshape2
 URL: https://github.com/IvanWilli/DemoKin
 BugReports: https://github.com/IvanWilli/DemoKin/issues
 Depends: 
diff --git a/R/data.R b/R/data.R
index 43be162..156b74c 100644
--- a/R/data.R
+++ b/R/data.R
@@ -1,5 +1,43 @@
-#' Female swedish survival ratios from 1900 to 2015
-#'
+#' UK female fertility from 1965 to 2022
+#' @docType data
+#' @format
+#'   list of age by stage matrices, entries give female fert. List starting 1965 ending 2022.
+#' @source
+#'   HFD and ONS
+"Female_parity_fert_list_UK"
+
+#' UK female parity transitions from 1965 to 2022
+#' @docType data
+#' @format
+#'   list of age by stage matrices, entries give female parity transitions. List starting 1965 ending 2022.
+#' @source
+#'   HFD and ONS
+"Parity_transfers_by_age_list_UK"
+
+#' UK female parity mortality from 1965 to 2022
+#' @docType data
+#' @format
+#'   list of age by stage matrices, entries give female parity mortality List starting 1965 ending 2022.
+#' @source
+#'   HFD and ONS
+"Female_parity_mortality_list_UK"
+
+#' UK male parity mortality from 1965 to 2022
+#' @docType data
+#' @format
+#'   list of age by stage matrices, entries give male parity mortality List starting 1965 ending 2022.
+#' @source
+#'   HFD and ONS
+"Male_parity_mortality_list_UK"
+
+#' UK parity assign parity at birth
+#' @docType data
+#' @format
+#'   list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
+#' @source
+#'   None
+"Redistribution_by_parity_list_UK"
+
 #' Female swedish survival ratios from 1900 to 2015
 #' @docType data
 #' @format
diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index 7b8c3e5..f52d3fa 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -34,8 +34,8 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                               H_list = NULL,
                                               birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
                                               parity = FALSE,
-                                              output_kin = FALSE, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
-                                              summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
+                                              output_kin = NULL, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
+                                              summary_kin = TRUE, # Set to FALSE if we want only a full age*stage distribution of kin
                                               sex_Focal = "Female", # Female Focal is default
                                               initial_stage_Focal = NULL,
                                               output_years){
@@ -45,7 +45,7 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
   ns <- ncol(U_list_females[[1]])
 
   # Ensure inputs are lists of matrices and that the timescale same length
-  if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
+  # if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
   if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
@@ -78,7 +78,6 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
     total = no_years + 1, clear = FALSE, width = 60)
   tictoc::tic()
   for(year in 1:no_years){
-    pb$tick()
     T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
     T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
     T_f_list <- list()
@@ -238,6 +237,7 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
     younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out[["cya"]]
     older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out[["coa"]]
     changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out[["ps"]]
+    pb$tick()
   }
   tictoc::toc()
   ## create a list of output kin -- each element a time-period specific list of matrices
@@ -260,23 +260,25 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
 
   relative_names <- names(relative_data)
   ## create a nice data frame output
+  kin_full <- create_full_dists_df(relative_data,
+                                   relative_names,
+                                   output_years,
+                                   output_years[1],
+                                   na,
+                                   ns,
+                                   output_kin)
   if(summary_kin){
-    kin_out <- create_cumsum_df(relative_data,
+    kin_summary <- create_cumsum_df(relative_data,
                                 relative_names,
-                                output_years[1]:output_years[length(output_years)],
+                                output_years,
                                 output_years[1],
                                 na,
                                 ns,
-                                output_kin)}
+                                output_kin)
+    kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)}
   else{
-    kin_out <- create_full_dists_df(relative_data,
-                                    relative_names,
-                                    output_years[1]:output_years[length(output_years)],
-                                    output_years[1],
-                                    na,
-                                    ns,
-                                    output_kin)}
-
+    kin_out <- kin_full
+  }
   return(kin_out)
 }
 
@@ -704,7 +706,7 @@ create_cumsum_df <- function(kin_matrix_lists,
                              start_year,
                              na,
                              ns,
-                             specific_kin){
+                             specific_kin = NULL){
   df_year_list <- list()
   for(j in years){
     ii <- as.numeric(j) - start_year + 1
@@ -746,7 +748,7 @@ create_cumsum_df <- function(kin_matrix_lists,
   df_year_list <- do.call("rbind", df_year_list)
   df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
                                                  cohort_factor = as.factor(cohort))
-  if(specific_kin != FALSE){
+  if(!is.null(specific_kin)){
     df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
   }
   return(df_year_list)
@@ -771,7 +773,7 @@ create_full_dists_df <- function(kin_matrix_lists,
                                  start_year,
                                  na,
                                  ns,
-                                 specific_kin){
+                                 specific_kin = NULL){
   df_year_list <- list()
   for(j in years){
     ii <- as.numeric(j) - start_year + 1
@@ -811,7 +813,7 @@ create_full_dists_df <- function(kin_matrix_lists,
   df_year_list <- do.call("rbind", df_year_list)
   df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
                                                  cohort_factor = as.factor(cohort))
-  if(specific_kin != FALSE){
+  if(!is.null(specific_kin)){
     df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
   }
   return(df_year_list)
diff --git a/R/kin_time_invariant_2sex.R b/R/kin_time_invariant_2sex.R
index e9d7822..6f733b6 100644
--- a/R/kin_time_invariant_2sex.R
+++ b/R/kin_time_invariant_2sex.R
@@ -105,15 +105,17 @@ kin_time_invariant_2sex <- function(pf = NULL, pm = NULL,
     ggm[,i+1]  = Ut %*% ggm[,i]
   }
 
-  # atribuible to focal sex
-  pios = if(sex_focal == "f") pif else pim
-  os[1:(agess),1]  = d[1:(agess),] %*% pif
-  nos[1:(agess),1] = gd[1:(agess),] %*% pif
+
+  # initial conditions on os and nos depends of focal sex
+  pios <- if(sex_focal == "f") pif else pim
+  os[1:(agess),1]  = d[1:(agess),] %*% pios
+  nos[1:(agess),1] = gd[1:(agess),] %*% pios
   for(i in 1:(ages-1)){
     os[,i+1]  = Ut %*% os[,i]
     nos[,i+1] = Ut %*% nos[,i] + Ft %*% os[,i]
   }
 
+  # continue
   oa[1:(agess),1]  = os[1:(agess),] %*% (pif + pim)
   ya[1:(agess),1]  = ys[1:(agess),] %*% (pif + pim)
   coa[1:(agess),1] = nos[1:(agess),] %*% (pif + pim)
diff --git a/R/kin_time_variant.R b/R/kin_time_variant.R
index 71a5dfa..0853b05 100644
--- a/R/kin_time_variant.R
+++ b/R/kin_time_variant.R
@@ -40,51 +40,52 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
   om           <- max(age)
   zeros        <- matrix(0, nrow=ages, ncol=ages)
 
-  # age distribution at childborn
-  pi_N_null_flag <- FALSE
+  # consider input data for age distribution at child born, or flag it
+  no_Pi <- FALSE
   if(is.null(pi)){
     if(is.null(n)){
       # create pi and fill it during the loop
-      message("Stable assumption was made for calculating pi on each year because no input data.")
-      pi_N_null_flag <- TRUE
+      no_Pi <- TRUE
       pi <- matrix(0, nrow=ages, ncol=n_years_data)
     }else{
-      pi_N_null_flag <- FALSE
+      no_Pi <- FALSE
       pi <- rbind(t(t(n * f)/colSums(n * f)), matrix(0,ages,length(years_data)))
     }
   }
 
-  # loop over years (more performance here)
+  # loop over years
   kin_all <- list()
   pb <- progress::progress_bar$new(
     format = "Running over input years [:bar] :percent",
     total = n_years_data + 1, clear = FALSE, width = 50)
   for (t in 1:n_years_data){
-    # build matrix
-    Ut = Mt = matrix(0, nrow=ages, ncol=ages)
+    # build set of matrix
+    Ut <- Mt <- matrix(0, nrow=ages, ncol=ages)
     Ut[row(Ut)-1 == col(Ut)] <- p[-ages,t]
-    Ut[ages, ages] = p[ages,t]
-    diag(Mt) = 1 - p[,t]
-    Ut = rbind(cbind(Ut,zeros),cbind(Mt,zeros))
-    ft = matrix(0, nrow=ages*2, ncol=ages*2)
-    ft[1,1:ages] = f[,t] * birth_female
-    if(pi_N_null_flag){
-      A <- Ut[1:ages,1:ages] + ft[1:ages,1:ages]
-      A_decomp = eigen(A)
-      w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
-      pit <- pi[,t] <- w*A[1,]/sum(w*A[1,])
-    }else{
-      pit <- pi[,t]
-    }
-    # proj
+    Ut[ages, ages] <- p[ages,t]
+    diag(Mt) <- 1 - p[,t]
+    Ut <- rbind(cbind(Ut,zeros),cbind(Mt,zeros))
+    ft <- matrix(0, nrow=ages*2, ncol=ages*2)
+    ft[1,1:ages] <- f[,t] * birth_female
+    A <- Ut[1:ages,1:ages] + ft[1:ages,1:ages]
+    # stable assumption at start
     if (t==1){
       p1 <- c(diag(Ut[-1,])[1:om],Ut[om,om])
-      f1 <- ft[1,][1:ages]
+      f1 <- ft[1,][1:ages]/birth_female
+      A_decomp <- eigen(A)
+      w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+      pit <- w*A[1,]/sum(w*A[1,])
       pi1 <- pit[1:ages]
-      kin_all[[1]] <- kin_time_invariant(p = p1, f = f1/birth_female, pi = pi1, birth_female = birth_female,
+      kin_all[[1]] <- kin_time_invariant(p = p1, f = f1, pi = pi1, birth_female = birth_female,
                                          list_output = TRUE)
     }
-    kin_all[[t+1]] <- timevarying_kin(Ut=Ut,ft=ft,pit=pit,ages,pkin=kin_all[[t]])
+    # project pi
+    if(no_Pi){
+      w <- A %*% w
+      pi[,t] <- w*A[1,]/sum(w*A[1,])
+    }
+    # kin for next year
+    kin_all[[t+1]] <- timevarying_kin(Ut = Ut, ft = ft, pit = pi[,t], ages, pkin = kin_all[[t]])
     pb$tick()
   }
 
@@ -93,7 +94,6 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
 
   # combinations to return
   out_selected <- output_period_cohort_combination(output_cohort, output_period, age = age, years_data = years_data)
-
   possible_kin <- c("d","gd","ggd","m","gm","ggm","os","ys","nos","nys","oa","ya","coa","cya")
   if(is.null(output_kin)){
     selected_kin_position <- 1:length(possible_kin)
@@ -138,6 +138,7 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
     out <- kin
   }
 
+  # end
   return(out)
 }
 
@@ -156,27 +157,27 @@ kin_time_variant <- function(p = NULL, f = NULL, pi = NULL, n = NULL,
 timevarying_kin<- function(Ut, ft, pit, ages, pkin){
 
   # frequently used zero vector for initial condition
-  zvec=rep(0,ages*2);
-  I = matrix(0, ages * 2, ages * 2)
-  diag(I[1:ages,1:ages]) = 1
-  om=ages-1;
-  d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0,ages*2,ages)
+  zvec <- rep(0,ages*2);
+  I <- matrix(0, ages * 2, ages * 2)
+  diag(I[1:ages,1:ages]) <- 1
+  om <- ages-1;
+  d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya <- matrix(0,ages*2,ages)
   kin_list <- list(d=d,gd=gd,ggd=ggd,m=m,gm=gm,ggm=ggm,os=os,ys=ys,
                     nos=nos,nys=nys,oa=oa,ya=ya,coa=coa,cya=cya)
 
   # initial distribution
-  d[,1]=gd[,1]=ggd[,1]=ys[,1]=nys[,1]=zvec
-  m[1:ages,1]  = pit[1:ages]
-  gm[1:ages,1] = pkin[["m"]][1:ages,] %*% pit[1:ages]
-  ggm[1:ages,1]= pkin[["gm"]][1:ages,] %*% pit[1:ages]
-  os[1:ages,1] = pkin[["d"]][1:ages,] %*% pit[1:ages]
+  d[,1] = gd[,1] = ggd[,1] = ys[,1] = nys[,1] = zvec
+  m[1:ages,1]   = pit[1:ages]
+  gm[1:ages,1]  = pkin[["m"]][1:ages,] %*% pit[1:ages]
+  ggm[1:ages,1] = pkin[["gm"]][1:ages,] %*% pit[1:ages]
+  os[1:ages,1]  = pkin[["d"]][1:ages,] %*% pit[1:ages]
   nos[1:ages,1] = pkin[["gd"]][1:ages,] %*% pit[1:ages]
-  oa[1:ages,1] = pkin[["os"]][1:ages,] %*% pit[1:ages]
-  ya[1:ages,1] = pkin[["ys"]][1:ages,] %*% pit[1:ages]
-  coa[1:ages,1]= pkin[["nos"]][1:ages,] %*% pit[1:ages]
-  cya[1:ages,1]= pkin[["nys"]][1:ages,] %*% pit[1:ages]
+  oa[1:ages,1]  = pkin[["os"]][1:ages,] %*% pit[1:ages]
+  ya[1:ages,1]  = pkin[["ys"]][1:ages,] %*% pit[1:ages]
+  coa[1:ages,1] = pkin[["nos"]][1:ages,] %*% pit[1:ages]
+  cya[1:ages,1] = pkin[["nys"]][1:ages,] %*% pit[1:ages]
 
-  # vers1
+  # focal´s trip
   for(ix in 1:om){
     d[,ix+1]   = Ut %*% pkin[["d"]][,ix]   + ft %*% I[,ix]
     gd[,ix+1]  = Ut %*% pkin[["gd"]][,ix]  + ft %*% pkin[["d"]][,ix]
diff --git a/R/kin_time_variant_2sex.R b/R/kin_time_variant_2sex.R
index e67db6f..7d76c09 100644
--- a/R/kin_time_variant_2sex.R
+++ b/R/kin_time_variant_2sex.R
@@ -48,9 +48,8 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
   om           <- max(age)
   zeros        <- matrix(0, nrow=ages, ncol=ages)
 
-  # age distribution at child born
+  # consider input data for age distribution at child born, or flag it to fill it
   Pif <- pif; no_Pif <- FALSE
-  Pim <- pim; no_Pim <- FALSE
   if(is.null(pif)){
     if(!is.null(nf)){
       Pif <- t(t(nf * ff)/colSums(nf * ff))
@@ -59,6 +58,7 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
       no_Pif <- TRUE
     }
   }
+  Pim <- pim; no_Pim <- FALSE
   if(is.null(pim)){
     if(!is.null(nm)){
       Pim <- t(t(nm * fm)/colSums(nm * fm))
@@ -69,65 +69,70 @@ kin_time_variant_2sex <- function(pf = NULL, pm = NULL,
   }
 
   # get lists of matrix
-  Ul = Fl = Fl_star = list()
+  Ul <- Fl <- Fl_star <- list()
   kin_all <- list()
   pb <- progress::progress_bar$new(
     format = "Running over input years [:bar] :percent",
     total = n_years_data + 1, clear = FALSE, width = 60)
   for(t in 1:n_years_data){
     # t = 1
-    Uf = Um = Fft = Fmt = Mm = Mf = Gt = zeros = matrix(0, nrow=ages, ncol=ages)
+    Uf = Um = Fft = Fmt = Mm = Mf = Gt = zeros <- matrix(0, nrow=ages, ncol=ages)
     Uf[row(Uf)-1 == col(Uf)] <- pf[-ages,t]
     Uf[ages, ages] = pf[ages,t]
     Um[row(Um)-1 == col(Um)] <- pm[-ages,t]
-    Um[ages, ages] = pm[ages,t]
+    Um[ages, ages] <- pm[ages,t]
     Mm <- diag(1-pm[,t])
     Mf <- diag(1-pf[,t])
     Ut <- as.matrix(rbind(
       cbind(Matrix::bdiag(Uf, Um), Matrix::bdiag(zeros, zeros)),
       cbind(Matrix::bdiag(Mf, Mm), Matrix::bdiag(zeros, zeros))))
     Ul[[as.character(years_data[t])]] <- Ut
-    Fft[1,] = ff[,t]
-    Fmt[1,] = fm[,t]
+    Fft[1,] <- ff[,t]
+    Fmt[1,] <- fm[,t]
     Ft <- Ft_star <- matrix(0, agess*2, agess*2)
     Ft[1:agess,1:agess] <- rbind(cbind(birth_female * Fft, birth_female * Fmt),
                                  cbind((1-birth_female) * Fft, (1-birth_female) * Fmt))
     Ft_star[1:agess,1:ages] <- rbind(birth_female * Fft, (1-birth_female) * Fft)
     Fl[[as.character(years_data[t])]] <- Ft
     Fl_star[[as.character(years_data[t])]] <- Ft_star
-    # parents age distribution under stable assumption in case no input
-    if(no_Pim | no_Pif){
-      A = Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
-      A_decomp = eigen(A)
-      lambda = as.double(A_decomp$values[1])
-      w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
-      wf = w[1:ages]
-      wm = w[(ages+1):(2*ages)]
-      Pif[,t] = wf * ff[,t] / sum(wf * ff[,t])
-      Pim[,t] = wm * fm[,t] / sum(wm * fm[,t])
-    }
+    A <- Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
 
     # project
     Ut <- as.matrix(Ul[[t]])
     Ft <- as.matrix(Fl[[t]])
     Ft_star <- as.matrix(Fl_star[[t]])
-    pitf <- Pif[,t]
-    pitm <- Pim[,t]
-    pit <- c(pitf, pitm)
+
+    # stable assumption at start
     if (t==1){
-      p1f <- pf[,1]
-      p1m <- pm[,1]
-      f1f <- ff[,1]
-      f1m <- fm[,1]
-      pif1 <- Pif[,1]
-      pim1 <- Pim[,1]
+      p1f <- pf[,1]; p1m <- pm[,1]
+      f1f <- ff[,1]; f1m <- fm[,1]
+      # time boundary for pi
+      A_decomp <- eigen(A)
+      lambda <- as.double(A_decomp$values[1])
+      w <- as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
+      wf <- w[1:ages]
+      wm <- w[(ages+1):(2*ages)]
+      pif1 <- wf * ff[,t] / sum(wf * ff[,t])
+      pim1 <- wm * fm[,t] / sum(wm * fm[,t])
       kin_all[[1]] <- kin_time_invariant_2sex(pf = p1f, pm = p1m,
                                               ff = f1f, fm = f1m,
                                               sex_focal = sex_focal,
                                               pif = pif1, pim = pim1,
                                               birth_female = birth_female, list_output = TRUE)
     }
-    kin_all[[t+1]] <- timevarying_kin_2sex(Ut=Ut, Ft=Ft, Ft_star=Ft_star, pit=pit, sex_focal, ages, pkin=kin_all[[t]])
+    # project pi
+    if(no_Pim | no_Pif){
+      w <- A %*% w
+      wf <- w[1:ages]
+      wm <- w[(ages+1):(2*ages)]
+      Pif[,t] <- wf * ff[,t] / sum(wf * ff[,t])
+      Pim[,t] <- wm * fm[,t] / sum(wm * fm[,t])
+    }
+    pit <- c(Pif[,t], Pim[,t])
+
+    # kin for next year
+    kin_all[[t+1]] <- timevarying_kin_2sex(Ut = Ut, Ft = Ft, Ft_star = Ft_star,
+                                           pit = pit, sex_focal, ages, pkin = kin_all[[t]])
     pb$tick()
   }
 
diff --git a/R/kin_time_variant_2sex_cod.R b/R/kin_time_variant_2sex_cod.R
index 562be20..b9f90ae 100644
--- a/R/kin_time_variant_2sex_cod.R
+++ b/R/kin_time_variant_2sex_cod.R
@@ -141,32 +141,20 @@ kin_time_variant_2sex_cod <- function(pf = NULL, pm = NULL,
     Ft_star[1:agess,1:ages] <- rbind(birth_female * Fft, (1-birth_female) * Fft)
     Fl[[as.character(years_data[t])]] <- Ft
     Fl_star[[as.character(years_data[t])]] <- Ft_star
-    # parents age distribution under stable assumption in case no input
-    if(no_Pim | no_Pif){
-      A = Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
+    A = Matrix::bdiag(Uf, Um) + Ft_star[1:agess,1:agess]
+
+    # stable assumption at start
+    if (t==1){
+      p1f <- pf[,1]; p1m <- pm[,1]
+      f1f <- ff[,1]; f1m <- fm[,1]
+      # time boundary for pi
       A_decomp = eigen(A)
       lambda = as.double(A_decomp$values[1])
       w = as.double(A_decomp$vectors[,1])/sum(as.double(A_decomp$vectors[,1]))
       wf = w[1:ages]
       wm = w[(ages+1):(2*ages)]
-      Pif[,t] = wf * ff[,t] / sum(wf * ff[,t])
-      Pim[,t] = wm * fm[,t] / sum(wm * fm[,t])
-    }
-
-    # project
-    Ut <- as.matrix(Ul[[t]])
-    Ft <- as.matrix(Fl[[t]])
-    Ft_star <- as.matrix(Fl_star[[t]])
-    pitf <- Pif[,t]
-    pitm <- Pim[,t]
-    pit <- c(pitf, pitm)
-    if (t==1){
-      p1f <- pf[,1]
-      p1m <- pm[,1]
-      f1f <- ff[,1]
-      f1m <- fm[,1]
-      pif1 <- Pif[,1]
-      pim1 <- Pim[,1]
+      pif1 = wf * ff[,t] / sum(wf * ff[,t])
+      pim1 = wm * fm[,t] / sum(wm * fm[,t])
 
       # BEN: Add Hf and Hm
       H1f <- Hf[[1]]
@@ -177,9 +165,22 @@ kin_time_variant_2sex_cod <- function(pf = NULL, pm = NULL,
                                                   ff = f1f, fm = f1m,
                                                   pif = pif1, pim = pim1,
                                                   Hf = H1f, Hm = H1m,
-                                              birth_female = birth_female, list_output = TRUE)
+                                                  birth_female = birth_female, list_output = TRUE)
     }
-    kin_all[[t+1]] <- timevarying_kin_2sex_cod(Ut=Ut, Ft=Ft, Ft_star=Ft_star, pit=pit, sex_focal, ages, pkin=kin_all[[t]])
+
+    # project pi
+    if(no_Pim | no_Pif){
+      w <- A %*% w
+      wf <- w[1:ages]
+      wm <- w[(ages+1):(2*ages)]
+      Pif[,t] <- wf * ff[,t] / sum(wf * ff[,t])
+      Pim[,t] <- wm * fm[,t] / sum(wm * fm[,t])
+    }
+    pit <- c(Pif[,t], Pim[,t])
+
+    # kin for next year
+    kin_all[[t+1]] <- timevarying_kin_2sex_cod(Ut=Ut, Ft=Ft, Ft_star=Ft_star, causes,
+                                               pit=pit, sex_focal, ages, pkin=kin_all[[t]])
     pb$tick()
   }
 
@@ -242,21 +243,22 @@ kin_time_variant_2sex_cod <- function(pf = NULL, pm = NULL,
 #' @param Ut numeric. A matrix of survival probabilities (or ratios).
 #' @param Ft numeric. A matrix of age-specific fertility rates.
 #' @param Ft_star numeric. Ft but for female fertility.
+#' @param causes integer. Number of causes of death included.
 #' @param pit numeric. A matrix with distribution of childbearing.
 #' @param sex_focal character. "f" for female or "m" for male.
 #' @param ages numeric.
 #' @param pkin numeric. A list with kin count distribution in previous year.
 #' @return A list of 14 types of kin matrices (kin age by Focal age, blocked for two sex) projected one time interval.
 #' @export
-timevarying_kin_2sex_cod<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin){
+timevarying_kin_2sex_cod<- function(Ut, Ft, Ft_star, causes, pit, sex_focal, ages, pkin){
 
   agess <- ages*2
   om <- ages-1
   pif <- pit[1:ages]
   pim <- pit[(ages+1):agess]
 
-  # BEN : Add the number of CoD
-  causes <- nrow(Hf[[1]])
+  # BEN : Add the number of CoD - IW: already as argument (Hf is not an argument)
+  # causes <- nrow(Hf[[1]])
 
   # BEN: Changed dimensions of lower part (dead kin) to account for death from causes.
   phi = d = gd = ggd = m = gm = ggm = os = ys = nos = nys = oa = ya = coa = cya = matrix(0, (agess + agess*causes), ages)
@@ -284,12 +286,14 @@ timevarying_kin_2sex_cod<- function(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
   m[1:agess,1]   = pit
   gm[1:agess,1]  = pkin[["m"]][1:agess,] %*% (pif + pim)
   ggm[1:agess,1] = pkin[["gm"]][1:agess,] %*% (pif + pim)
-  os[1:agess,1]  = pkin[["d"]][1:agess,] %*% pif
-  nos[1:agess,1] = pkin[["gd"]][1:ages,] %*% pif
   oa[1:agess,1]  = pkin[["os"]][1:agess,] %*% (pif + pim)
   ya[1:agess,1]  = pkin[["ys"]][1:agess,] %*% (pif + pim)
   coa[1:agess,1] = pkin[["nos"]][1:agess,] %*% (pif + pim)
   cya[1:agess,1] = pkin[["nys"]][1:agess,] %*% (pif + pim)
+  # atribuible to focal sex
+  pios <- if(sex_focal == "f") pif else pim
+  os[1:agess,1]  = pkin[["d"]][1:agess,] %*% pios
+  nos[1:agess,1] = pkin[["gd"]][1:ages,] %*% pios
 
   for (ix in 1:om){
     phi[,ix+1] = Gt %*% phi[, ix]
diff --git a/README.Rmd b/README.Rmd
index a3f462f..a817bde 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -1,8 +1,9 @@
 ---
 output: github_document
-bibliography: vignettes\\references.bib
 ---
 
+<!-- bibliography: vignettes\\references.bib -->
+
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
@@ -91,7 +92,7 @@ If the vignette does not load, you may need to install the package as `devtools:
 
 ## Citation
 
-Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: https://github.com/IvanWilli/DemoKin.
+Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: https://github.com/IvanWilli/DemoKin.
 
 ## Acknowledgments
 
@@ -103,4 +104,4 @@ Williams, Iván; Alburez-Gutierrez, Diego; Song, Xi; and Hal Caswell. (2021) Dem
 If you're interested in contributing, please get in touch, create an issue, or submit a pull request.
 We look forward to hearing from you!
 
-## References
+<!-- ## References -->
diff --git a/README.md b/README.md
index 9255f10..cee8664 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,6 @@
 
+<!-- bibliography: vignettes\\references.bib -->
+
 # DemoKin
 
 <div class="columns">
@@ -8,8 +10,10 @@
 `DemoKin` uses matrix demographic methods to compute expected (average)
 kin counts from demographic rates under a range of scenarios and
 assumptions. The package is an R-language implementation of Caswell
-(2019, 2020, 2022), and Caswell and Song (2021). It draws on previous
-theoretical development by Goodman, Keyfitz and Pullum (1974).
+\[-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2022\],
+and Caswell and Song \[-@caswell_formal_2021\]. It draws on previous
+theoretical development by Goodman, Keyfitz and Pullum
+\[-@goodman_family_1974\].
 
 </div>
 
@@ -42,7 +46,8 @@ devtools::install_github("IvanWilli/DemoKin")
 Consider an average Swedish woman called ‘Focal.’ For this exercise, we
 assume a female closed population in which everyone experiences the
 Swedish 2015 mortality and fertility rates at each age throughout their
-life; i.e., the ‘time-invariant’ assumption in Caswell (2019).
+life; i.e., the ‘time-invariant’ assumption in Caswell
+\[-@caswell_formal_2019\].
 
 We then ask:
 
@@ -84,7 +89,7 @@ Relatives are identified by a unique code:
 | coa     | Cousins from older aunts    | Cousins from older uncles     | Cousins from older aunts/uncles   |
 | cya     | Cousins from younger aunts  | Cousins from younger uncles   | Cousins from younger aunts/uncles |
 | c       | Cousins                     | Cousins                       | Cousins                           |
-| d       | Daughters                   | Brothers                      | Siblings                          |
+| d       | Daughters                   | Sons                          | Children                          |
 | gd      | Grand-daughters             | Grand-sons                    | Grand-childrens                   |
 | ggd     | Great-grand-daughters       | Great-grand-sons              | Great-grand-childrens             |
 | ggm     | Great-grandmothers          | Great-grandfathers            | Great-grandfparents               |
@@ -116,9 +121,9 @@ does not load, you may need to install the package as
 
 ## Citation
 
-Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team.
-(2021) DemoKin: An R package to implement demographic matrix kinship
-models. URL: <https://github.com/IvanWilli/DemoKin>.
+Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team. (2021)
+DemoKin: An R package to implement demographic matrix kinship models.
+URL: <https://github.com/IvanWilli/DemoKin>.
 
 ## Acknowledgments
 
@@ -136,50 +141,4 @@ Commons. Sha Jiang provided useful comments for improving the package.
 contributing, please get in touch, create an issue, or submit a pull
 request. We look forward to hearing from you!
 
-## References
-
-<div id="refs" class="references csl-bib-body hanging-indent">
-
-<div id="ref-caswell_formal_2019" class="csl-entry">
-
-Caswell, Hal. 2019. “The Formal Demography of Kinship: A Matrix
-Formulation.” *Demographic Research* 41 (September): 679–712.
-<https://doi.org/10.4054/DemRes.2019.41.24>.
-
-</div>
-
-<div id="ref-caswell_formal_2020" class="csl-entry">
-
-———. 2020. “The Formal Demography of Kinship II: Multistate Models,
-Parity, and Sibship.” *Demographic Research* 42 (June): 1097–1146.
-<https://doi.org/10.4054/DemRes.2020.42.38>.
-
-</div>
-
-<div id="ref-caswell_formal_2022" class="csl-entry">
-
-———. 2022. “The Formal Demography of Kinship IV: Two-Sex Models and
-Their Approximations.” *Demographic Research* 47 (September): 359–96.
-<https://doi.org/10.4054/DemRes.2022.47.13>.
-
-</div>
-
-<div id="ref-caswell_formal_2021" class="csl-entry">
-
-Caswell, Hal, and Xi Song. 2021. “The Formal Demography of Kinship III:
-Kinship Dynamics with Time-Varying Demographic Rates.” *Demographic
-Research* 45 (August): 517–46.
-<https://doi.org/10.4054/DemRes.2021.45.16>.
-
-</div>
-
-<div id="ref-goodman_family_1974" class="csl-entry">
-
-Goodman, Leo A, Nathan Keyfitz, and Thomas W. Pullum. 1974. “Family
-Formation and the Frequency of Various Kinship Relationships.”
-*Theoretical Population Biology*, 27.
-<https://doi.org/10.1016/0040-5809(74)90049-5>.
-
-</div>
-
-</div>
+<!-- ## References -->
diff --git a/man/Female_parity_fert_list_UK.Rd b/man/Female_parity_fert_list_UK.Rd
new file mode 100644
index 0000000..854be41
--- /dev/null
+++ b/man/Female_parity_fert_list_UK.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{Female_parity_fert_list_UK}
+\alias{Female_parity_fert_list_UK}
+\title{UK female fertility from 1965 to 2022}
+\format{
+list of age by stage matrices, entries give female fert. List starting 1965 ending 2022.
+}
+\source{
+HFD and ONS
+}
+\usage{
+Female_parity_fert_list_UK
+}
+\description{
+UK female fertility from 1965 to 2022
+}
+\keyword{datasets}
diff --git a/man/Female_parity_mortality_list_UK.Rd b/man/Female_parity_mortality_list_UK.Rd
new file mode 100644
index 0000000..3e0eef1
--- /dev/null
+++ b/man/Female_parity_mortality_list_UK.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{Female_parity_mortality_list_UK}
+\alias{Female_parity_mortality_list_UK}
+\title{UK female parity mortality from 1965 to 2022}
+\format{
+list of age by stage matrices, entries give female parity mortality List starting 1965 ending 2022.
+}
+\source{
+HFD and ONS
+}
+\usage{
+Female_parity_mortality_list_UK
+}
+\description{
+UK female parity mortality from 1965 to 2022
+}
+\keyword{datasets}
diff --git a/man/Male_parity_mortality_list_UK.Rd b/man/Male_parity_mortality_list_UK.Rd
new file mode 100644
index 0000000..316ca71
--- /dev/null
+++ b/man/Male_parity_mortality_list_UK.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{Male_parity_mortality_list_UK}
+\alias{Male_parity_mortality_list_UK}
+\title{UK male parity mortality from 1965 to 2022}
+\format{
+list of age by stage matrices, entries give male parity mortality List starting 1965 ending 2022.
+}
+\source{
+HFD and ONS
+}
+\usage{
+Male_parity_mortality_list_UK
+}
+\description{
+UK male parity mortality from 1965 to 2022
+}
+\keyword{datasets}
diff --git a/man/Parity_transfers_by_age_list_UK.Rd b/man/Parity_transfers_by_age_list_UK.Rd
new file mode 100644
index 0000000..babf3e9
--- /dev/null
+++ b/man/Parity_transfers_by_age_list_UK.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{Parity_transfers_by_age_list_UK}
+\alias{Parity_transfers_by_age_list_UK}
+\title{UK female parity transitions from 1965 to 2022}
+\format{
+list of age by stage matrices, entries give female parity transitions. List starting 1965 ending 2022.
+}
+\source{
+HFD and ONS
+}
+\usage{
+Parity_transfers_by_age_list_UK
+}
+\description{
+UK female parity transitions from 1965 to 2022
+}
+\keyword{datasets}
diff --git a/man/Redistribution_by_parity_list_UK.Rd b/man/Redistribution_by_parity_list_UK.Rd
new file mode 100644
index 0000000..2d81919
--- /dev/null
+++ b/man/Redistribution_by_parity_list_UK.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{Redistribution_by_parity_list_UK}
+\alias{Redistribution_by_parity_list_UK}
+\title{UK parity assign parity at birth}
+\format{
+list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
+}
+\source{
+None
+}
+\usage{
+Redistribution_by_parity_list_UK
+}
+\description{
+UK parity assign parity at birth
+}
+\keyword{datasets}
diff --git a/man/create_cumsum_df.Rd b/man/create_cumsum_df.Rd
index 28bc914..920d482 100644
--- a/man/create_cumsum_df.Rd
+++ b/man/create_cumsum_df.Rd
@@ -11,7 +11,7 @@ create_cumsum_df(
   start_year,
   na,
   ns,
-  specific_kin
+  specific_kin = NULL
 )
 }
 \arguments{
diff --git a/man/create_full_dists_df.Rd b/man/create_full_dists_df.Rd
index 94fdb0e..2e4e0c8 100644
--- a/man/create_full_dists_df.Rd
+++ b/man/create_full_dists_df.Rd
@@ -11,7 +11,7 @@ create_full_dists_df(
   start_year,
   na,
   ns,
-  specific_kin
+  specific_kin = NULL
 )
 }
 \arguments{
diff --git a/man/kin_multi_stage_time_variant_2sex.Rd b/man/kin_multi_stage_time_variant_2sex.Rd
index 82aed18..e62d30f 100644
--- a/man/kin_multi_stage_time_variant_2sex.Rd
+++ b/man/kin_multi_stage_time_variant_2sex.Rd
@@ -14,7 +14,7 @@ kin_multi_stage_time_variant_2sex(
   H_list = NULL,
   birth_female = 0.49,
   parity = FALSE,
-  output_kin = FALSE,
+  output_kin = NULL,
   summary_kin = TRUE,
   sex_Focal = "Female",
   initial_stage_Focal = NULL,
diff --git a/man/timevarying_kin_2sex_cod.Rd b/man/timevarying_kin_2sex_cod.Rd
index 8bc0b87..fb82bad 100644
--- a/man/timevarying_kin_2sex_cod.Rd
+++ b/man/timevarying_kin_2sex_cod.Rd
@@ -4,7 +4,7 @@
 \alias{timevarying_kin_2sex_cod}
 \title{one time projection kin}
 \usage{
-timevarying_kin_2sex_cod(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
+timevarying_kin_2sex_cod(Ut, Ft, Ft_star, causes, pit, sex_focal, ages, pkin)
 }
 \arguments{
 \item{Ut}{numeric. A matrix of survival probabilities (or ratios).}
@@ -13,6 +13,8 @@ timevarying_kin_2sex_cod(Ut, Ft, Ft_star, pit, sex_focal, ages, pkin)
 
 \item{Ft_star}{numeric. Ft but for female fertility.}
 
+\item{causes}{integer. Number of causes of death included.}
+
 \item{pit}{numeric. A matrix with distribution of childbearing.}
 
 \item{sex_focal}{character. "f" for female or "m" for male.}
diff --git a/tests/testthat/test-kin_twosex_multistate_timevariant.R b/tests/testthat/test-kin_twosex_multistate_timevariant.R
index 1dcea27..dbb5697 100644
--- a/tests/testthat/test-kin_twosex_multistate_timevariant.R
+++ b/tests/testthat/test-kin_twosex_multistate_timevariant.R
@@ -30,12 +30,12 @@ test_that("same output in multi_stage (caswell 2020)", {
                                                   list(Tm),
                                                   list(H),
                                                   birth_female = 0.49, ## svk_fxs already divided
-                                                  output_kin = FALSE,
+                                                  output_kin = NULL,
                                                   parity = TRUE,
                                                   summary_kin = FALSE,
                                                   sex_Focal = "Female", ##  define Focal's sex at birth
                                                   initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                                  seq(1990, (1990)))
+                                                  output_years = seq(1990, (1990)))
 
 
   ## Younger sisters
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 0e5de95..e1295f8 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -10,7 +10,7 @@ vignette: >
 ---
 
 ```{r, eval = T, include=FALSE}
-knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
 library(devtools); load_all()
 ```
 
@@ -31,11 +31,7 @@ We seek the number of, age, and stage distribution of Focal's relatives, for eac
 # library(DemoKin)
 library(Matrix)
 library(tictoc)
-`%>%` <- magrittr::`%>%`
-
-
 options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise output (but also #lose #progress bar)
-
 ```
 ### Kin counts by parity  ###
 
@@ -76,11 +72,8 @@ To avoid the need for tedious calculations to put data into such format in this
 in another file and simply imported below. The code below reads in the above function input lists.
 
 ```{r eval=TRUE, message=FALSE, warning=FALSE, include=TRUE}
-
-## files loaded as "rda" extensions (from "data/)
-
 F_mat_fem <- Female_parity_fert_list_UK
-F_mat_male <- Male_parity_fert_list_UK
+F_mat_male <- Female_parity_fert_list_UK
 T_mat_fem <- Parity_transfers_by_age_list_UK
 T_mat_male <- Parity_transfers_by_age_list_UK
 U_mat_fem <- Female_parity_mortality_list_UK
@@ -113,18 +106,18 @@ H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0
 ### 1. Accumulated number of kin Focal expects over the lifecourse under time-varying rates from 1965 to 2005 ###
 
 We feed the above inputs into the matrix model, along with other arguments:
-UK sex ratio --> `birth_female` = 0.49
-We are considering parity --> `parity` = TRUE
-We want all of Focal's kin network --> `output_kin` = FALSE
-Accumulated kin in this example --> `summary_kin` = TRUE
-Focal is female --> `sex_Focal` = "Female"
-Focal born into parity 0 --> `initial_stage_Focal` = 1
-timescale from 1965-1985 -- > `output_years` = seq(1965, 1965 + 40)
+
+-   UK sex ratio --> `birth_female` = 0.49
+-   We are considering parity --> `parity` = TRUE
+-   We want some of Focal's kin network --> `output_kin` = c("d", "oa", "ys", "os")
+-   Accumulated kin in this example --> `summary_kin` = TRUE
+-   Focal is female --> `sex_Focal` = "Female"
+-   Focal born into parity 0 --> `initial_stage_Focal` = 1
+-   timescale as ouptut -- > `output_years` = c(1965, 1975, 1985, 1995, 2005)
 
 Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage 
 distribution of kin. 
 
-Notice that the timescale argument `output_years` = seq(1965,2005) gives a sequence of 1965, 1966, ..., 2004, 2005 of length 41. 
 The first sets of time-varying vital rates in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965), 
 the 41-st entry is U_mat_fem[[(1+40)]] (mortality in 2005). We require consistency between the length of the list of vital rates
 and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005). Therefore we use the input lists of demographic rates
@@ -132,6 +125,8 @@ and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005). Therefore
 `U_list_females` = U_mat_fem[1:(1+no_years)] which runs from U_mat_fem[[1]] = 1965 set of rates, up to U_mat_fem[[41]] = 2005 set of rates, 
 and so on...
 
+> this run takes some time (round 10 min) so we don´t include the output in the vignette. Please try it!
+
 ```{r, message=FALSE, warning=FALSE}
 # Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
 no_years <- 40
@@ -147,18 +142,19 @@ kin_out_1965_2005 <-
                                     H_list = H_mat[1:(1+no_years)],
                                     birth_female = 1 - 0.51, ## Sex ratio -- UK value
                                     parity = TRUE,
-                                    output_kin = FALSE,
+                                    output_kin = c("d", "oa", "ys", "os"),
                                     summary_kin = TRUE,
                                     sex_Focal = "Female", ##  define Focal's sex at birth
                                     initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
-  )
+                                    output_years = c(1965, 1975, 1985, 1995, 2005) ## the sequence of years we run the function over
+)
+
 ```
 ### 1.1. Visualizing the output ###
 
 ```{r, message=FALSE, warning=FALSE}
 
-head(kin_out_1965_2005)
+head(kin_out_1965_2005$kin_summary)
 ```
 
 Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin, 
@@ -179,9 +175,8 @@ below plot is that we really plot Focal's born into different `cohort` -- i.e.,
 while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
-kin_out_1965_2005 %>%
-  dplyr::filter(group == "oa",
-                year %in% c(1965, 1975, 1985, 1995, 2005)) %>%
+kin_out_1965_2005$kin_summary %>%
+  dplyr::filter(group == "oa") %>%
   ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
   ggplot2::facet_grid(sex_kin ~ year) +
@@ -193,9 +188,8 @@ kin_out_1965_2005 %>%
 We could also consider any other kin in Focal's network, for instance, offspring using `group` == "d"
 
 ```{r, fig.height=6, fig.width=8}
-kin_out_1965_2005 %>%
-  dplyr::filter(group == "d",
-                year %in% c(1965, 1975, 1985, 1995, 2005)) %>%
+kin_out_1965_2005$kin_summary %>%
+  dplyr::filter(group == "d") %>%
   ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
   ggplot2::facet_grid(sex_kin ~ year) +
@@ -210,7 +204,7 @@ Since we only ran the model for 40 years (between 1965-2005), there is very litt
 We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring and only show Focals born of `cohort` 1910, 1925, or 1965:
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005 %>%
+kin_out_1965_2005$kin_summary %>%
   dplyr::filter(group == "d", cohort %in% c(1910,1925,1965) ) %>%
   ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
@@ -228,35 +222,13 @@ the RHS plot (1965 cohort) simply reflects the fact that Focal will not start re
 
 ### 2. Now lets consider the distributions of kin Focal expects over the lifecourse ###
 
-To obtain distributions of kin as output, we simply change the function argument: `summary_kin` = FALSE
+To obtain distributions of kin as output, we simply use the `kin_full` data.frame.
 
-```{r, message=FALSE, warning=FALSE}
-rm(kin_out_1965_2005)
-gc()
-no_years <- 40
-
-kin_out_1965_2005_full <-
-  kin_multi_stage_time_variant_2sex(U_mat_fem[1:(1+no_years)],
-                                    U_mat_male[1:(1+no_years)],
-                                    F_mat_fem[1:(1+no_years)],
-                                    F_mat_male[1:(1+no_years)],
-                                    T_mat_fem[1:(1+no_years)],
-                                    T_mat_fem[1:(1+no_years)],
-                                    H_mat[1:(1+no_years)],
-                                    birth_female = 1 - 0.51, ## Sex ratio -- UK value
-                                    parity = TRUE,
-                                    output_kin = FALSE,
-                                    summary_kin = FALSE,
-                                    sex_Focal = "Female", ##  define Focal's sex at birth
-                                    initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
-)
-```
 ### 2.1. Visualizing the output ###
 
 ```{r, message=FALSE, warning=FALSE}
 
-head(kin_out_1965_2005_full)
+head(kin_out_1965_2005$kin_full)
 ```
 
 Notice the additional column `age_kin`. Rather than grouping kin by stage and summing over all ages,
@@ -270,9 +242,8 @@ Restricting ourselves to the years 1965, 1975, 1985, 1995, 2005, we can plot the
 of these kin over the considered periods, as shown below:
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005_full %>%
+kin_out_1965_2005$kin_full %>%
   dplyr::filter(group == "ys",
-                year %in% c(1965, 1975, 1985, 1995, 2005),
                 age_focal == 50) %>%
   ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
@@ -284,14 +255,13 @@ kin_out_1965_2005_full %>%
   ggplot2::ggtitle("Focal 50")
 ```
 
-Notice the discontinuity along the x-abissca at 50. This reflects the fact that Focal's younger siblings 
+Notice the discontinuity along the x-abscissa at 50. This reflects the fact that Focal's younger siblings 
 cannot are of age <50. Contrastingly, when we look at the age*stage distribution of older siblings, we observe another
 discontinuity which bounds kin to be of age >50, as plotted below:
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005_full %>%
+kin_out_1965_2005$kin_full %>%
   dplyr::filter(group == "os",
-                year %in% c(1965, 1975, 1985, 1995, 2005),
                 age_focal == 50) %>%
   ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
@@ -306,9 +276,8 @@ kin_out_1965_2005_full %>%
 With a simple bit of playing with the output data frame, we can plot the age*stage distribution of the combined siblings of Focal
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005_full %>%
+kin_out_1965_2005$kin_full %>%
   dplyr::filter((group == "ys" | group == "os"),
-                year %in% c(1965, 1975, 1985, 1995, 2005),
                 age_focal == 50) %>%
   tidyr::pivot_wider(names_from = group, values_from = count) %>%
   dplyr::mutate(count = `ys` + `os`) %>%

From a860fba2dbb93c6426a8271d3a806fb05c2c2b90 Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Fri, 21 Feb 2025 11:12:38 +0000
Subject: [PATCH 59/89] fixing age-year increments

---
 R/kin_multi_stage_time_variant_2sex.R         |   51 +-
 R/kin_multi_stage_time_variant_2sex_preJANG.R | 1048 +++++++++++++++++
 2 files changed, 1081 insertions(+), 18 deletions(-)
 create mode 100644 R/kin_multi_stage_time_variant_2sex_preJANG.R

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index 7b8c3e5..8255a5b 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -38,14 +38,16 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                               summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
                                               sex_Focal = "Female", # Female Focal is default
                                               initial_stage_Focal = NULL,
-                                              output_years){
+                                              output_years,
+                                              age_year_consitent = TRUE,
+                                              age_increment = NULL){
 
   no_years <- length(U_list_females)
   na <- nrow(U_list_females[[1]])
   ns <- ncol(U_list_females[[1]])
-
+  if(age_year_consitent){age_increment <- as.numeric(output_years[2]-output_years[1])}
   # Ensure inputs are lists of matrices and that the timescale same length
-  if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
+  if(length(U_list_females) < (length(output_years))){stop("Proposed timescale longer than demographic timescale")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
   if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
@@ -263,19 +265,21 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
   if(summary_kin){
     kin_out <- create_cumsum_df(relative_data,
                                 relative_names,
-                                output_years[1]:output_years[length(output_years)],
+                                output_years,
                                 output_years[1],
                                 na,
                                 ns,
-                                output_kin)}
+                                output_kin,
+                                age_increment)}
   else{
     kin_out <- create_full_dists_df(relative_data,
                                     relative_names,
-                                    output_years[1]:output_years[length(output_years)],
+                                    output_years,
                                     output_years[1],
                                     na,
                                     ns,
-                                    output_kin)}
+                                    output_kin,
+                                    age_increment)}
 
   return(kin_out)
 }
@@ -704,25 +708,30 @@ create_cumsum_df <- function(kin_matrix_lists,
                              start_year,
                              na,
                              ns,
-                             specific_kin){
+                             specific_kin = NULL,
+                             increment = NULL){
+  if(length(years) > length(kin_matrix_lists[[1]])){stop("More years than data")}
+  age_inc <- increment
   df_year_list <- list()
   for(j in years){
-    ii <- as.numeric(j) - start_year + 1
+    ii <- which(years == j)
     df_list <- list()
     for(i in 1 : length(kin_names)){
       kin_member <- kin_names[[i]]
       kin_data <- kin_matrix_lists[[i]]
       kin_data <- kin_data[[ii]]
       df <- as.data.frame(as.matrix(kin_data))
-      dims <- dim( kin_data)
+      dims <- dim( kin_data )
       nr <- dims[1]
       nc <- dims[2]
       female_kin <- df[1:(nr/2), 1:nc]
       male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      colnames(female_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
+      colnames(male_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
       female_kin$stage <- rep(seq(1, ns), na)
       male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1)), each = ns)
-      male_kin$age <- rep(seq(0, (na-1)), each = ns)
+      female_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
+      male_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
       female_kin$Sex <- "Female"
       male_kin$Sex <- "Male"
       both_kin <- rbind(female_kin, male_kin)
@@ -734,7 +743,7 @@ create_cumsum_df <- function(kin_matrix_lists,
                                                 stage_kin = as.factor(stage),
                                                 count = num,
                                                 sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal)) - 1
+      both_kin$age_focal <- as.numeric(paste(both_kin$age_focal))
       df <- both_kin
       df$year <- j
       df$group <- kin_member
@@ -771,11 +780,14 @@ create_full_dists_df <- function(kin_matrix_lists,
                                  start_year,
                                  na,
                                  ns,
-                                 specific_kin){
+                                 specific_kin = NULL,
+                                 increment = NULL){
+  if(length(years) > length(kin_matrix_lists[[1]])){stop("More years than data")}
+  age_inc <- increment
   df_year_list <- list()
   for(j in years){
-    ii <- as.numeric(j) - start_year + 1
     df_list <- list()
+    ii <- which(years == j)
     for(i in 1 : length(kin_names)){
       kin_member <- kin_names[[i]]
       kin_data <- kin_matrix_lists[[i]]
@@ -786,10 +798,12 @@ create_full_dists_df <- function(kin_matrix_lists,
       nc <- dims[2]
       female_kin <- df[1:(nr/2), 1:nc]
       male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      colnames(female_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
+      colnames(male_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
       female_kin$stage <- rep(seq(1, ns), na)
       male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1)), each = ns)
-      male_kin$age <- rep(seq(0, (na-1)), each = ns)
+      female_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
+      male_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
       female_kin$Sex <- "Female"
       male_kin$Sex <- "Male"
       both_kin <- rbind(female_kin, male_kin)
@@ -799,7 +813,7 @@ create_full_dists_df <- function(kin_matrix_lists,
                          stage_kin = as.factor(stage),
                          count = value,
                          sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal))-1
+      both_kin$age_focal <- as.numeric(paste(both_kin$age_focal))
       df <- both_kin
       df$year <- j
       df$group <- kin_member
@@ -807,6 +821,7 @@ create_full_dists_df <- function(kin_matrix_lists,
     }
     df_list <- do.call("rbind", df_list)
     df_year_list[[(1+length(df_year_list))]] <- df_list
+
   }
   df_year_list <- do.call("rbind", df_year_list)
   df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
diff --git a/R/kin_multi_stage_time_variant_2sex_preJANG.R b/R/kin_multi_stage_time_variant_2sex_preJANG.R
new file mode 100644
index 0000000..ad413e6
--- /dev/null
+++ b/R/kin_multi_stage_time_variant_2sex_preJANG.R
@@ -0,0 +1,1048 @@
+
+
+#' Estimate kin counts by age, stage, and sex, in a time variant framework
+
+#' @description Implementation of combined formal demographic models: Caswell II,III,IV.
+
+#' @param U_list_females list with matrix entries: period-specific female survival probabilities. Age in rows and states in columns.
+#' @param U_list_males list with matrix entries: period-specific male survival probabilities. Age in rows and states in columns.
+#' @param F_list_females list with matrix with elements: period-specific female fertility (age in rows and states in columns).
+#' @param F_list_males list with matrix entries: period-specific male fertility (age in rows and states in columns).
+#' @param T_list_females list of lists with matrix entries: each outer list entry is period-specific, and composed of
+#'                     a list of stochastic matrices which describe age-specific female probabilities of transferring stage
+#' @param T_list_males list of lists with matrix entries: each outer list entry is period-specific, and composed of
+#'                     a list of stochastic matrices which describe age-specific male probabilities of transferring stage
+#' @param H_list list with matrix entries: redistribution of newborns across each stage to a specific age-class
+#' @param birth_female numeric. birth ratio of females to males in population
+#' @param parity logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default `TRUE`.
+#' @param output_kin vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.
+#' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if TRUE, and kin by their age*stage distribution by age of Focal if FALSE.
+#' @param sex_Focal character. Female or Male as the user requests.
+#' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
+#' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
+#'
+#' @return A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
+
+#' @export
+#'
+kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
+                                              U_list_males = NULL,
+                                              F_list_females = NULL,
+                                              F_list_males = NULL,
+                                              T_list_females = NULL,
+                                              T_list_males = NULL,
+                                              H_list = NULL,
+                                              birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
+                                              parity = FALSE,
+                                              output_kin = FALSE, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
+                                              summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
+                                              sex_Focal = "Female", # Female Focal is default
+                                              initial_stage_Focal = NULL,
+                                              output_years){
+
+  no_years <- length(U_list_females)
+  na <- nrow(U_list_females[[1]])
+  ns <- ncol(U_list_females[[1]])
+
+  # Ensure inputs are lists of matrices and that the timescale same length
+  if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
+  if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+  if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+  if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+
+  ### Define empty lists for the accumulated kin of Focals's life-course -- each list entry will reflect a time-period
+  changing_pop_struct <- list()
+  Focal_array <- list()
+  mom_array <- list()
+  gran_array <- list()
+  great_gran_array <- list()
+  daughter_array <- list()
+  younger_sis_array <- list()
+  grand_daughter_array <-list()
+  great_grand_daughter_array <- list()
+  older_sister_array <- list()
+  younger_aunt_array <- list()
+  older_aunt_array <- list()
+  younger_niece_array <- list()
+  older_niece_array <- list()
+  younger_cousin_array <- list()
+  older_cousin_array <- list()
+
+  ### At each time-period we: 1) -- construct the time-variant projection matrices:
+  ###                               U_tilde : transfers across stage and advances age
+  ###                               F_tilde : makes newborns from stage/age; puts them to stage/age
+  ###                         2) -- project Focal and kin using above projection matrices
+
+  pb <- progress::progress_bar$new(
+    format = "Timescale [:bar] :percent",
+    total = no_years + 1, clear = FALSE, width = 60)
+  tictoc::tic()
+  for(year in 1:no_years){
+    pb$tick()
+    T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
+    T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
+    T_f_list <- list()
+    T_m_list <- list()
+    F_f_list <- list()
+    F_m_list <- list()
+    U_f_list <- list()
+    U_m_list <- list()
+    H_list2 <- list()
+
+    for(stage in 1:ns){
+      Uf <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      Matrix::diag(Uf[-1,-ncol(Uf)]) <- U_list_females[[year]][1:(na-1),stage]
+      Uf[na,na] <- U_list_females[[year]][na,stage]
+      Um <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      Matrix::diag(Um[-1,-ncol(Um)]) <- U_list_males[[year]][1:(na-1),stage]
+      Um[na,na] <- U_list_males[[year]][na,stage]
+      U_f_list[[(1+length(U_f_list))]] <- Uf
+      U_m_list[[(1+length(U_m_list))]] <- Um
+      H_mat <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      H_mat[1,] <- 1
+      H_list2[[(1+length(H_list2))]] <- H_mat
+    }
+    for(age in 1:na){
+      T_f <- T_data_f[[age]]
+      T_m <- T_data_m[[age]]
+      T_f_list[[(1+length(T_f_list))]] <- T_f
+      T_m_list[[(1+length(T_m_list))]] <- T_m
+      F_f <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
+      F_m <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
+      F_f[1,] <- F_list_females[[year]][age,]
+      F_m[1,] <- F_list_males[[year]][age,]
+      F_f_list[[(1+length(F_f_list))]] <- F_f
+      F_m_list[[(1+length(F_m_list))]] <- F_m
+    }
+    ## create the appropriate block-diagonal matrices
+    U_f_BDD <- block_diag_function(U_f_list) ## direct sum of female survivorship, independent over stage (ns diagonal blocks)
+    U_m_BDD <- block_diag_function(U_m_list) ## direct sum of male survivorship, independent over stage (ns diagonal blocks)
+    H_BDD <- block_diag_function(H_list2) ## direct sum of which age newborns enter, independent over stage (ns diagonal blocks)
+    T_f_BDD <- block_diag_function(T_f_list) ## direct sum of female stage transitions, independent over age (na diagonal blocks)
+    T_m_BDD <- block_diag_function(T_m_list) ## direct sum of male stage transitions, independent over age (na diagonal blocks)
+    F_f_BDD <- block_diag_function(F_f_list) ## direct sum of female stage->stage reproductions, independent over age (na blocks)
+    F_m_BDD <- block_diag_function(F_m_list) ## direct sum of male stage->stage reproductions, independent over age (na blocks)
+
+    ## create the appropriate projection matrices
+    U_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
+      U_f_BDD %*%
+      K_perm_mat(ns, na) %*%
+      T_f_BDD
+
+    ## create sex-specific age*stage projections
+    U_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
+      U_m_BDD %*%
+      K_perm_mat(ns, na) %*%
+      T_m_BDD
+
+    F_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
+      H_BDD %*%
+      K_perm_mat(ns, na) %*%
+      F_f_BDD
+
+    F_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
+      H_BDD %*%
+      K_perm_mat(ns, na) %*%
+      F_m_BDD
+
+    ## if year == 1 we are at the boundary condition t=0 apply time-invariant kinship projections
+    if(year == 1){
+      ## Output of the static model
+      kin_out_1 <- all_kin_dy(U_tilde_females,
+                              U_tilde_males ,
+                              F_tilde_females,
+                              F_tilde_males,
+                              1-birth_female,
+                              na,
+                              ns,
+                              parity,
+                              sex_Focal,
+                              initial_stage_Focal)
+      ### Relative lists' first entries
+      Focal_array[[(1+length(Focal_array))]] <- kin_out_1[["Focal"]]
+      daughter_array[[(1+length(daughter_array))]] <- kin_out_1[["d"]]
+      grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out_1[["gd"]]
+      great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out_1[["ggd"]]
+      mom_array[[(1+length(mom_array))]] <- kin_out_1[["m"]]
+      gran_array[[(1+length(gran_array))]] <- kin_out_1[["gm"]]
+      great_gran_array[[(1+length(great_gran_array))]] <- kin_out_1[["ggm"]]
+      younger_sis_array[[( 1+length(younger_sis_array))]] <- kin_out_1[["ys"]]
+      older_sister_array[[(1+length(older_sister_array))]] <- kin_out_1[["os"]]
+      younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out_1[["ya"]]
+      older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out_1[["oa"]]
+      younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out_1[["nys"]]
+      older_niece_array[[(1+length(older_niece_array))]] <- kin_out_1[["nos"]]
+      younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out_1[["cya"]]
+      older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out_1[["coa"]]
+      changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out_1[["ps"]]
+
+    }
+    updating_Focal <- Focal_array[[year]]
+    updating_daughter <- daughter_array[[year]]
+    updating_grand_daughter <- grand_daughter_array[[year]]
+    updating_great_grand_daughter <- great_grand_daughter_array[[year]]
+    updating_mom <- mom_array[[year]]
+    updating_gran <- gran_array[[year]]
+    updating_great_gran <- great_gran_array[[year]]
+    updating_younger_sis <- younger_sis_array[[year]]
+    updating_older_sis <- older_sister_array[[year]]
+    updating_youner_aunt <- younger_aunt_array[[year]]
+    updating_older_aunt <- older_aunt_array[[year]]
+    updating_younger_niece <- younger_niece_array[[year]]
+    updating_older_niece <- older_niece_array[[year]]
+    updating_younger_cousin <- younger_cousin_array[[year]]
+    updating_older_cousin <- older_cousin_array[[year]]
+    updating_pop_struct <- changing_pop_struct[[year]]
+
+    ## Output of the time-variant model
+    kin_out <- all_kin_dy_TV(U_tilde_females,
+                             U_tilde_males,
+                             F_tilde_females,
+                             F_tilde_males,
+                             1-birth_female,
+                             na,
+                             ns,
+                             parity,
+                             sex_Focal,
+                             initial_stage_Focal,
+                             updating_Focal,
+                             updating_daughter,
+                             updating_grand_daughter,
+                             updating_great_grand_daughter,
+                             updating_mom,
+                             updating_gran,
+                             updating_great_gran,
+                             updating_older_sis,
+                             updating_younger_sis,
+                             updating_older_niece,
+                             updating_younger_niece,
+                             updating_older_aunt,
+                             updating_youner_aunt,
+                             updating_older_cousin,
+                             updating_younger_cousin,
+                             updating_pop_struct)
+    ## Relative lists entries correspond to timescale periods (each entry an kin age*stage*2 by Focal age matrix)
+    Focal_array[[(1+length(Focal_array))]] <- kin_out[["Focal"]]
+    daughter_array[[(1+length(daughter_array))]] <- kin_out[["d"]]
+    grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out[["gd"]]
+    great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out[["ggd"]]
+    mom_array[[(1+length(mom_array))]] <- kin_out[["m"]]
+    gran_array[[(1+length(gran_array))]] <- kin_out[["gm"]]
+    great_gran_array[[(1+length(great_gran_array))]] <- kin_out[["ggm"]]
+    younger_sis_array[[(1+length(younger_sis_array))]] <- kin_out[["ys"]]
+    older_sister_array[[(1+length(older_sister_array))]] <- kin_out[["os"]]
+    younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out[["ya"]]
+    older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out[["oa"]]
+    younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out[["nys"]]
+    older_niece_array[[(1+length(older_niece_array))]] <- kin_out[["nos"]]
+    younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out[["cya"]]
+    older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out[["coa"]]
+    changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out[["ps"]]
+  }
+  tictoc::toc()
+  ## create a list of output kin -- each element a time-period specific list of matrices
+  ## label the kin names to match DemoKin:
+  relative_data <- list("Focal" = Focal_array,
+                        "d" = daughter_array,
+                        "gd" = grand_daughter_array,
+                        "ggd" = great_grand_daughter_array,
+                        "m" = mom_array,
+                        "gm" = gran_array,
+                        "ggm" = great_gran_array,
+                        "ys" = younger_sis_array,
+                        "os" = older_sister_array,
+                        "ya" = younger_aunt_array,
+                        "oa" = older_aunt_array,
+                        "nys" = younger_niece_array,
+                        "nos" = older_niece_array,
+                        "cya" = younger_cousin_array,
+                        "coa" = older_cousin_array)
+
+  relative_names <- names(relative_data)
+  ## create a nice data frame output
+  if(summary_kin){
+    kin_out <- create_cumsum_df(relative_data,
+                                relative_names,
+                                output_years[1]:output_years[length(output_years)],
+                                output_years[1],
+                                na,
+                                ns,
+                                output_kin)}
+  else{
+    kin_out <- create_full_dists_df(relative_data,
+                                    relative_names,
+                                    output_years[1]:output_years[length(output_years)],
+                                    output_years[1],
+                                    na,
+                                    ns,
+                                    output_kin)}
+
+  return(kin_out)
+}
+
+
+#' Title time invariant two-sex multi-state kin projections
+#'
+#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
+#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
+#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
+#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
+#' @param alpha scalar. birth ratio (male:female)
+#' @param na scalar. number of ages.
+#' @param ns scalar. number of stages.
+#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
+#' @param sex_Focal logical. Female or Male
+#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
+#'
+#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+#' yielding the age*stage distribution of kin for each age of Focal
+
+all_kin_dy <- function(Uf,
+                       Um,
+                       Ff,
+                       Fm,
+                       alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
+                       na, ## na = number of ages
+                       ns, ## ns = number of stages
+                       Parity,
+                       sex_Focal, ## binary "F" or "M"
+                       Initial_stage_Focal){
+
+  n <- nrow(Uf) ## number of ages * stages for each sex
+
+  ## Projection matrices:
+
+  ## Uproj is a block diagonal matrix of block-structured Age*Stage matrices; independently over sex transfers individuals across stage and up age
+  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
+  ## Fproj is a Sex-block-structured matrix of block-structured Age*Stage matrices where males and females BOTH reproduce (by stage)
+  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  Fproj[1:n, 1:n] <- (1-alpha)*Ff ## Ff is Age*Stage block structured giving rate at which females in age-stage produce individuals in age-stage
+  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
+  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm ## Fm is Age*Stage block structured giving rate at which males in age-stage produce individuals in age-stage
+  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
+
+  ## Fprojstar is a Sex-block-structured matrix of block-structured Age*Stage matrices where ONLY females reproduce
+  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
+  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
+  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
+
+  ## The stable population structure is an age*stage*sex vector:
+  ##                                                            1:n gives the female age*stage structure
+  ##                                                            (1+n):2n gives the male age*stage structure
+  population_age_stage_structure <- SD(Uproj + Fprojstar)
+
+  ### Stable distribution of mothers needs adjusting if we work with parity
+  if(Parity){
+    Initial_stage_Focal <- 1
+
+    population_age_stage_of_parenting <- pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+  else{
+    population_age_stage_of_parenting <- pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+
+  ####################################### The dynamics of Kinship, starting with Focal who is no longer a unit vector
+
+  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
+  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
+  m_t <- get_G(Um, na, ns)
+  G_tilde <- block_diag_function(list(f_t,m_t))
+  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  IC_Focal <- rep(0, 2*n)
+  if(sex_Focal == "Female"){
+    entry <- 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1}
+  else{
+    entry <- n + 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1
+  }
+
+  ### empty kin matrices for all of Focal's kin
+  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_aunt_uncle  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+
+
+  ### Initial distributions for kin with non-zero deterministic initial conditions:
+  # Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
+  X_Focal[,1] <- IC_Focal
+  X_parents[, 1] <- mothers_age_stage
+
+  ### projection all kin with deterministic initial conditions
+  for(i in 1 : (na-1)){
+    X_Focal[,i+1] <- G_tilde %*% X_Focal[,i]
+    X_parents[, i+1] <- Uproj %*% X_parents[, i]
+    X_younger_sibs[,i+1] <- Uproj %*% X_younger_sibs[,i] + Fprojstar %*% X_parents[,i]
+    X_younger_niece_nephew[,i+1] <- Uproj %*% X_younger_niece_nephew[,i] + Fproj %*% X_younger_sibs[,i]
+    X_children[,i+1] <- Uproj %*% X_children[,i] + Fproj %*% X_Focal[,i]
+    X_grand_children[,i+1] <- Uproj %*% X_grand_children[,i] + Fproj %*% X_children[,i]
+    X_great_grand_children[,i+1] <- Uproj %*% X_great_grand_children[,i] + Fproj %*% X_grand_children[,i]
+  }
+
+  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
+  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
+  IC_f_grand_pars <- mothers_age_dist
+  IC_m_grand_pars <- fathers_age_dist
+  IC_f_great_grand_pars <- mothers_age_dist
+  IC_m_great_grand_pars <- fathers_age_dist
+  IC_older_sibs_f <- mothers_age_dist
+  IC_younger_aunts_uncles_f <- mothers_age_dist
+  IC_younger_aunts_uncles_m <- fathers_age_dist
+  IC_older_niece_nephew_f <- mothers_age_dist
+  for(ic in 1 : (na)){
+    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*X_parents[,ic] ## IC the sum of parents of Focal's parents,
+    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*X_grand_parents[,ic]
+    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*X_children[,ic]
+    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*X_grand_children[,ic]
+    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*X_younger_sibs[,ic]
+  }
+
+  ### Projections of grand parenst, older sibs, younger aunts/uncles, older nieces/nephews
+  for(i in 1: (na-1)){
+    X_grand_parents[, i+1] <- Uproj %*% X_grand_parents[, i]
+    X_great_grand_parents[, i+1] <- Uproj %*% X_great_grand_parents[, i]
+    X_older_sibs[,i+1] <- Uproj %*% X_older_sibs[,i]
+    X_older_niece_nephew[,i+1] <- Uproj %*% X_older_niece_nephew[,i] + Fproj %*% X_older_sibs[,i]
+    X_younger_aunts_uncles[,i+1] <- Uproj %*% X_younger_aunts_uncles[,i] + Fprojstar %*% X_grand_parents[,i]
+  }
+
+  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
+  ## older unts/uncles, older cousins, younger cousins
+  IC_older_aunt_uncle_f <- mothers_age_dist
+  IC_older_aunt_uncle_m <- fathers_age_dist
+  IC_older_cousins_f <- mothers_age_dist
+  IC_older_cousins_m <- fathers_age_dist
+  IC_younger_cousins_f <- mothers_age_dist
+  IC_younger_cousins_m <- fathers_age_dist
+  for(ic in 1 : (na-1)){
+    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*X_older_sibs[,ic]
+    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*X_older_niece_nephew[,ic]
+    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*X_younger_niece_nephew[,ic]
+  }
+
+  ## Projections of older unts/uncles, older cousins, younger cousins
+  for(i in 1: (na-1)){
+    X_older_aunt_uncle[,i+1] <- Uproj %*% X_older_aunt_uncle[,i]
+    X_older_cousins[,i+1] <- Uproj %*% X_older_cousins[,i] + Fproj %*% X_older_aunt_uncle[,i]
+    X_younger_cousins[,i+1] <- Uproj %*% X_younger_cousins[,i] + Fproj %*% X_younger_aunts_uncles[,i]
+  }
+
+  #### OUTPUT of all kin
+  return(list("Focal" = X_Focal,
+              "d" = X_children,
+              "gd" = X_grand_children,
+              "ggd" = X_great_grand_children,
+              "m" = X_parents,
+              "gm" = X_grand_parents,
+              "ggm" = X_great_grand_parents,
+              "os" = X_older_sibs,
+              "ys" = X_younger_sibs,
+              "nos" = X_older_niece_nephew,
+              "nys" = X_younger_niece_nephew,
+              "oa" = X_older_aunt_uncle,
+              "ya" = X_younger_aunts_uncles,
+              "coa" = X_older_cousins,
+              "cya" = X_younger_cousins,
+              "ps" = population_age_stage_structure
+  ))
+}
+
+
+#' Title time-variant two-sex multi-state kin projections
+#'
+#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
+#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
+#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
+#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
+#' @param alpha scalar. birth ratio (male:female)
+#' @param na scalar. number of ages.
+#' @param ns scalar. number of stages.
+#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
+#' @param sex_Focal logical. Female or Male
+#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
+#' @param previous_kin_Focal matrix. last years kinship output.
+#' @param prev_kin_children matrix. last years kinship output.
+#' @param prev_kin_grandchildren matrix. last years kinship output.
+#' @param prev_kin_greatgrandchildren matrix. last years kinship output.
+#' @param prev_kin_parents matrix. last years kinship output.
+#' @param prev_kin_grand_parents matrix. last years kinship output.
+#' @param prev_kin_older_sibs matrix. last years kinship output.
+#' @param prev_kin_younger_sibs matrix. last years kinship output.
+#' @param prev_kin_older_niece_nephew matrix. last years kinship output.
+#' @param prev_kin_younger_niece_nephew matrix. last years kinship output.
+#' @param prev_kin_older_aunts_uncles matrix. last years kinship output.
+#' @param prev_kin_younger_aunts_uncles matrix. last years kinship output.
+#' @param prev_kin_older_cousins matrix. last years kinship output.
+#' @param prev_kin_younger_cousins matrix. last years kinship output.
+#' @param previous_population_age_stage_structure vector. The transient "population structure" (age*stage distributed)
+#'
+#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+#' yielding the age*stage distribution of kin for each age of Focal
+#'
+all_kin_dy_TV <- function(Uf,
+                          Um,
+                          Ff,
+                          Fm,
+                          alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
+                          na, ## number of ages
+                          ns, ## number of stages
+                          Parity,
+                          sex_Focal,
+                          Initial_stage_Focal,
+                          previous_kin_Focal,
+                          prev_kin_children,
+                          prev_kin_grandchildren,
+                          prev_kin_greatgrandchildren,
+                          prev_kin_parents,
+                          prev_kin_grand_parents,
+                          prev_kin_great_grand_parents,
+                          prev_kin_older_sibs,
+                          prev_kin_younger_sibs,
+                          prev_kin_older_niece_nephew,
+                          prev_kin_younger_niece_nephew,
+                          prev_kin_older_aunts_uncles,
+                          prev_kin_younger_aunts_uncles,
+                          prev_kin_older_cousins,
+                          prev_kin_younger_cousins,
+                          previous_population_age_stage_structure){
+
+  n <- nrow(Uf)
+  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
+  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  Fproj[1:n, 1:n] <- (1-alpha)*Ff
+  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
+  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm
+  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
+  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
+  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
+  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
+
+  population_age_stage_structure <- previous_population_age_stage_structure
+  population_age_stage_structure <- population_age_stage_structure/sum(population_age_stage_structure)
+  population_age_stage_structure_next <- (Uproj + Fprojstar)%*%population_age_stage_structure
+
+  ### Stable distribution of mothers needs adjusting if we work with parity
+  if(Parity){
+    Initial_stage_Focal <- 1
+
+    population_age_stage_of_parenting <- pi_mix_TV_parity(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+  else{
+
+    population_age_stage_of_parenting <- pi_mix_TV(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+
+  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
+  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
+  m_t <- get_G(Um, na, ns)
+  G_tilde <- block_diag_function(list(f_t,m_t))
+  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  IC_Focal <- rep(0, 2*n)
+  if(sex_Focal == "Female"){
+    entry <- 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1}
+  else{
+    entry <- n + 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1
+  }
+
+  ### empty kin matrices for all of Focal's kin
+  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_aunt_uncle <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+
+  ### Initial distributions for kin with non-zero deterministic initial conditions:
+  ## Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
+  X_Focal[,1] <- IC_Focal
+  X_parents[, 1] <- mothers_age_stage
+  ### projection all above kin with deterministic initial conditions
+  for(i in 1 : (na-1)){
+    X_Focal[,i+1] <- G_tilde %*% previous_kin_Focal[,i]
+    X_parents[, i+1] <- Uproj %*% prev_kin_parents[, i]
+    X_younger_sibs[,i+1] <- Uproj %*% prev_kin_younger_sibs[,i] + Fprojstar %*% prev_kin_parents[,i]
+    X_younger_niece_nephew[,i+1] <- Uproj %*% prev_kin_younger_niece_nephew[,i] + Fproj %*% prev_kin_younger_sibs[,i]
+    X_children[,i+1] <- Uproj %*% prev_kin_children[,i] + Fproj %*% previous_kin_Focal[,i]
+    X_grand_children[,i+1] <- Uproj %*% prev_kin_grandchildren[,i] + Fproj %*% prev_kin_children[,i]
+    X_great_grand_children[,i+1] <- Uproj %*% prev_kin_greatgrandchildren[,i] + Fproj %*% prev_kin_grandchildren[,i]
+  }
+
+  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
+  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
+  IC_f_grand_pars <- mothers_age_dist
+  IC_m_grand_pars <- fathers_age_dist
+  IC_f_great_grand_pars <- mothers_age_dist
+  IC_m_great_grand_pars <- fathers_age_dist
+  IC_younger_aunts_uncles_f <- mothers_age_dist
+  IC_younger_aunts_uncles_m <- fathers_age_dist
+  IC_older_sibs_f <- mothers_age_dist
+  IC_older_niece_nephew_f <- mothers_age_dist
+  for(ic in 1 : (na)){
+    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*prev_kin_parents[,ic] ## IC the sum of parents of Focal's parents,
+    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*prev_kin_grand_parents[,ic]
+    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*prev_kin_children[,ic]
+    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*prev_kin_grandchildren[,ic]
+    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*prev_kin_younger_sibs[,ic]
+  }
+
+  ### Projections of older sibs, younger aunts/uncles, older nieces/nephews
+  for(i in 1: (na-1)){
+    X_grand_parents[, i+1] <- Uproj %*% prev_kin_grand_parents[, i]
+    X_great_grand_parents[, i+1] <- Uproj %*% prev_kin_great_grand_parents[, i]
+    X_older_sibs[,i+1] <- Uproj %*% prev_kin_older_sibs[,i]
+    X_older_niece_nephew[,i+1] <- Uproj %*% prev_kin_older_niece_nephew[,i] + Fproj %*% prev_kin_older_sibs[,i]
+    X_younger_aunts_uncles[,i+1] <- Uproj %*% prev_kin_younger_aunts_uncles[,i] + Fprojstar %*% prev_kin_grand_parents[,i]
+  }
+
+  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
+  ## older unts/uncles, older cousins, younger cousins
+  IC_older_aunt_uncle_f <- mothers_age_dist
+  IC_older_aunt_uncle_m <- fathers_age_dist
+  IC_older_cousins_f <- mothers_age_dist
+  IC_older_cousins_m <- fathers_age_dist
+  IC_younger_cousins_f <- mothers_age_dist
+  IC_younger_cousins_m <- fathers_age_dist
+  for(ic in 1 : (na-1)){
+    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*prev_kin_older_sibs[,ic]
+    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*prev_kin_older_niece_nephew[,ic]
+    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*prev_kin_younger_niece_nephew[,ic]
+  }
+
+  ## Projections of older unts/uncles, older cousins, younger cousins
+  for(i in 1: (na-1)){
+    X_older_aunt_uncle[,i+1] <- Uproj %*% prev_kin_older_aunts_uncles[,i]
+    X_older_cousins[,i+1] <- Uproj %*% prev_kin_older_cousins[,i] + Fproj %*% prev_kin_older_aunts_uncles[,i]
+    X_younger_cousins[,i+1] <- Uproj %*% prev_kin_younger_cousins[,i] + Fproj %*% prev_kin_younger_aunts_uncles[,i]
+  }
+
+  return(list("Focal" = X_Focal,
+              "d" = X_children,
+              "gd" = X_grand_children,
+              "ggd" = X_great_grand_children,
+              "m" = X_parents,
+              "gm" = X_grand_parents,
+              "ggm" = X_great_grand_parents,
+              "os" = X_older_sibs,
+              "ys" = X_younger_sibs,
+              "nos" = X_older_niece_nephew,
+              "nys" = X_younger_niece_nephew,
+              "oa" = X_older_aunt_uncle,
+              "ya" = X_younger_aunts_uncles,
+              "coa" = X_older_cousins,
+              "cya" = X_younger_cousins,
+              "ps" = population_age_stage_structure_next))
+}
+
+################## Create data frame output
+
+## Use of "pipe" (don't understand the name, but hey)
+`%>%` <- magrittr::`%>%`
+
+#' Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
+#'
+#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
+#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
+#' @param years vector. The timescale on which we implement the kinship model.
+#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
+#' @param na numeric. Number of ages.
+#' @param ns numeric. Number of stages.
+#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
+#'
+#' @return A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
+#'
+create_cumsum_df <- function(kin_matrix_lists,
+                             kin_names,
+                             years,
+                             start_year,
+                             na,
+                             ns,
+                             specific_kin = NULL){
+  df_year_list <- list()
+  for(j in years){
+    ii <- as.numeric(j) - start_year + 1
+    df_list <- list()
+    for(i in 1 : length(kin_names)){
+      kin_member <- kin_names[[i]]
+      kin_data <- kin_matrix_lists[[i]]
+      kin_data <- kin_data[[ii]]
+      df <- as.data.frame(as.matrix(kin_data))
+      dims <- dim( kin_data)
+      nr <- dims[1]
+      nc <- dims[2]
+      female_kin <- df[1:(nr/2), 1:nc]
+      male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      female_kin$stage <- rep(seq(1, ns), na)
+      male_kin$stage <- rep(seq(1, ns), na)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
+      female_kin$Sex <- "Female"
+      male_kin$Sex <- "Male"
+      both_kin <- rbind(female_kin, male_kin)
+      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
+        dplyr::group_by(variable, stage, Sex) %>%
+        dplyr::summarise(num = sum(value)) %>%
+        dplyr::ungroup()
+      both_kin <- both_kin %>% dplyr::transmute(age_focal = variable,
+                                                stage_kin = as.factor(stage),
+                                                count = num,
+                                                sex_kin = Sex)
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal)) - 1
+      df <- both_kin
+      df$year <- j
+      df$group <- kin_member
+      df_list[[length(df_list)+1]] <- df
+    }
+    df_list <- do.call("rbind", df_list)
+    df_year_list[[(1+length(df_year_list))]] <- df_list
+  }
+  df_year_list <- do.call("rbind", df_year_list)
+  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
+                                                 cohort_factor = as.factor(cohort))
+  if(specific_kin != FALSE){
+    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
+  }
+  return(df_year_list)
+}
+
+#' Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
+#'
+#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
+#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
+#' @param years vector. The timescale on which we implement the kinship model.
+#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
+#' @param na numeric. Number of ages.
+#' @param ns numeric. Number of stages.
+#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
+#'
+#' @return A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
+#'
+create_full_dists_df <- function(kin_matrix_lists,
+                                 kin_names,
+                                 years,
+                                 start_year,
+                                 na,
+                                 ns,
+                                 specific_kin = NULL){
+  df_year_list <- list()
+  for(j in years){
+    ii <- as.numeric(j) - start_year + 1
+    df_list <- list()
+    for(i in 1 : length(kin_names)){
+      kin_member <- kin_names[[i]]
+      kin_data <- kin_matrix_lists[[i]]
+      kin_data <- kin_data[[ii]]
+      df <- as.data.frame(as.matrix(kin_data))
+      dims <- dim( kin_data)
+      nr <- dims[1]
+      nc <- dims[2]
+      female_kin <- df[1:(nr/2), 1:nc]
+      male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      female_kin$stage <- rep(seq(1, ns), na)
+      male_kin$stage <- rep(seq(1, ns), na)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
+      female_kin$Sex <- "Female"
+      male_kin$Sex <- "Male"
+      both_kin <- rbind(female_kin, male_kin)
+      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
+        dplyr::transmute(age_focal = variable,
+                         age_kin = age,
+                         stage_kin = as.factor(stage),
+                         count = value,
+                         sex_kin = Sex)
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal))-1
+      df <- both_kin
+      df$year <- j
+      df$group <- kin_member
+      df_list[[length(df_list)+1]] <- df
+    }
+    df_list <- do.call("rbind", df_list)
+    df_year_list[[(1+length(df_year_list))]] <- df_list
+  }
+  df_year_list <- do.call("rbind", df_year_list)
+  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
+                                                 cohort_factor = as.factor(cohort))
+  if(specific_kin != FALSE){
+    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
+  }
+  return(df_year_list)
+}
+
+
+
+## Construct a matrix composed as a direct sum of a list of matrices
+block_diag_function <- function(mat_list){
+  s = length(mat_list)
+  u1 = mat_list[[1]]
+  dims <- dim(u1)
+  r = dims[1]
+  diagmat <- Matrix::Matrix(nrow = (r*s), ncol = (r*s), data = 0, sparse = TRUE)
+  for(i in 1:s){
+    diagmat = diagmat + kronecker(E_matrix(i,i,s,s), mat_list[[i]])
+  }
+  return(diagmat)
+}
+
+## Construct a matrix which transfers Focal across stages, while ensuring Focal survives with probability 1
+get_G <- function(U, na, ns){
+  sig <- Matrix::t(rep(1,na*ns)) %*% U
+  diag <- Matrix::diag(sig[1,])
+  G <- U %*% MASS::ginv(diag)
+  return(G)
+}
+
+#' Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix <- function(Uf, Um, Ff, Fm, alpha, na, ns){
+  n <- length(Uf[1,])
+  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  F_block[1:n, 1:n] <- (1-alpha)*Ff
+  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
+  A <- block_diag_function(list(Uf,Um)) + F_block
+  stable_dist_vec <- SD(A)
+  ### Joint distributions
+  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  ### Age distributions
+  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
+  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
+  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+#' Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
+#'
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_TV <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
+  n <- length(Ff[1,])
+  ### Joint distributions
+  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  ### Age distributions
+  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
+  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
+  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+#' Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_parity <- function(Uf, Um, Ff, Fm, alpha, na, ns){
+  n <- length(Uf[1,])
+  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  F_block[1:n, 1:n] <- (1-alpha)*Ff
+  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
+  A <- block_diag_function(list(Uf,Um)) + F_block
+  stable_dist_vec <- SD(A)
+  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  m_mat <- pi_f %*% Matrix::t(rep(1,na))
+  d_mat <- pi_m %*% Matrix::t(rep(1,na))
+  pi_F <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
+  pi_M <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
+  for(i in 1:na){
+    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
+    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
+  }
+  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
+  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
+  ### Joint distributions
+  pi_f <- out_mum %*% pi_F
+  pi_m <- out_dad %*% pi_M
+  return(list(c(pi_f,pi_m), pi_f, pi_m, pi_F, pi_M))
+}
+
+#' Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_TV_parity <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
+  n <- length(Ff[1,])
+  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  m_mat <- pi_f %*% Matrix::t(rep(1,na))
+  d_mat <- pi_m %*% Matrix::t(rep(1,na))
+  pi_F  <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
+  pi_M <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
+  for(i in 1:na){
+    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
+    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
+  }
+  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
+  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
+  ### Joint distributions
+  pi_f <- out_mum %*% pi_F
+  pi_m <- out_dad %*% pi_M
+  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+
+######################################################### Some useful utility functions required
+
+
+###################################################### Eigen-decomposition of a matrix
+
+# Calculate the spectral radius of a matrix (growth rate in Demographics)
+lambda <- function(PM) {
+  lead_eig <- (abs(eigen(PM, only.values = TRUE)$values))
+  lead_eig <- lead_eig[which.max(lead_eig)]
+  return(lead_eig)
+}
+# Find the column-eigenvector corresponding to the spectral radius (Stable population structure in Demographics)
+SD <- function(PM) {
+  spectral_stuff <- eigen(PM)
+  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
+  # normalise...
+  vec_lambda <- spectral_stuff/sum(spectral_stuff)
+  return(vec_lambda)
+}
+# Find the row-eigenvector corresponding to the spectral radius (Stable reproductive values in Demographics)
+RD <- function(PM) {
+  spectral_stuff <- eigen(t(PM))
+  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
+  # normalise...
+  vec_lambda <- spectral_stuff/sum(spectral_stuff)
+  return(vec_lambda)
+}
+
+###################################################### Useful matrix operations
+
+## Constructing a unit vector with a 1 in the ith position
+e_vector <- function(i, n){
+  e <- rep(0, n)
+  e[i] <- 1
+  return(e)
+}
+## Creating a matrix of zeros with a 1 in the i,j-th entry
+E_matrix <- function(i,j,n,m){
+  E <- Matrix::Matrix(nrow = (n), ncol = (m), data = 0, sparse = TRUE)
+  E[i,j] <- 1
+  return(E)
+
+}
+## Creating the Vec-commutation matrix
+K_perm_mat <- function(n,m){
+  perm <- Matrix::Matrix(nrow = (n*m), ncol = (n*m), data = 0, sparse = TRUE)
+  for(i in 1:n){
+    for(j in 1:m){
+      perm = perm + kronecker( E_matrix(i,j,n,m) , Matrix::t(E_matrix(i,j,n,m)) )
+    }
+  }
+  return(perm)
+}
+
+
+
+
+
+
+
+

From 4ce63245577bac86ad581deb7535a617f4edc361 Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Fri, 21 Feb 2025 11:29:34 +0000
Subject: [PATCH 60/89] fixing age-year increments

---
 R/kin_multi_stage_time_variant_2sex.R         |  26 +--
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 159 +++++++-----------
 2 files changed, 79 insertions(+), 106 deletions(-)

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index 8255a5b..e1bc8ff 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -262,25 +262,27 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
 
   relative_names <- names(relative_data)
   ## create a nice data frame output
+  kin_full <- create_full_dists_df(relative_data,
+                                   relative_names,
+                                   output_years,
+                                   output_years[1],
+                                   na,
+                                   ns,
+                                   output_kin,
+                                   age_increment)
   if(summary_kin){
-    kin_out <- create_cumsum_df(relative_data,
-                                relative_names,
-                                output_years,
-                                output_years[1],
-                                na,
-                                ns,
-                                output_kin,
-                                age_increment)}
-  else{
-    kin_out <- create_full_dists_df(relative_data,
+    kin_summary <- create_cumsum_df(relative_data,
                                     relative_names,
                                     output_years,
                                     output_years[1],
                                     na,
                                     ns,
                                     output_kin,
-                                    age_increment)}
-
+                                    age_increment)
+    kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)}
+  else{
+    kin_out <- kin_full
+  }
   return(kin_out)
 }
 
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 1346e1f..66872b0 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -10,38 +10,34 @@ vignette: >
 ---
 
 ```{r, eval = T, include=FALSE}
-knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
 library(devtools); load_all()
 ```
 
-Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model 
-of kinship, there have been many extensions to the framework (many of which are documented within this package). 
-Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates, 
-Caswell [-@caswell_formal_2022] introduced two-sexes to the model, 
-and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin. 
-Here, we provide an R function which combines the three aforementioned models. 
+Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model
+of kinship, there have been many extensions to the framework (many of which are documented within this package).
+Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates,
+Caswell [-@caswell_formal_2022] introduced two-sexes to the model,
+and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin.
+Here, we provide an R function which combines the three aforementioned models.
 
-In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks 
-encompassing both sexes for an average member of a population, the sex of whom is user specified, 
-and who is subject to time-varying demographic rates. We call this individual Focal. 
+In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks
+encompassing both sexes for an average member of a population, the sex of whom is user specified,
+and who is subject to time-varying demographic rates. We call this individual Focal.
 We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
 
 
-```{R}
-library(DemoKin)
+```{r}
+# library(DemoKin)
 library(Matrix)
 library(tictoc)
-`%>%` <- magrittr::`%>%`
-
-
 options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise output (but also #lose #progress bar)
-
 ```
 ### Kin counts by parity  ###
 
-In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from 
-the [Human Mortality Database](https://www.mortality.org/) 
-and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). 
+In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from
+the [Human Mortality Database](https://www.mortality.org/)
+and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/).
 Some simplifying assumptions we make due to data availability are as follows:
 
 i) Fertility rates vary with time, are distinct among parity class, but the same over sexes (the so-called ``androgynous approximation'').
@@ -63,24 +59,21 @@ This input list has length = the timescale, and each entry represents the rates
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
 
 5) `T_list_females` A list of lists of female age-specific probabilities of moving up parity over the timescale (in matrix forms).
-The outer list has length = the timescale. The inner list has length = number of ages. 
-Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage. 
+The outer list has length = the timescale. The inner list has length = number of ages.
+Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage.
 Thus for each year, we have a list of age-specific probabilities of moving from one stage to the next.
 
 6) Same as 5) but for males
 
-7) `H_list` A list of length = timescale, in which each element is a matrix which assigns the offspring of individuals in some stage to 
+7) `H_list` A list of length = timescale, in which each element is a matrix which assigns the offspring of individuals in some stage to
 the appropriate age class (age in rows and states in columns)
 
-To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed 
+To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed
 in another file and simply imported below. The code below reads in the above function input lists.
 
-```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
-
-## files loaded as "rda" extensions (from "data/)
-
+```{r eval=TRUE, message=FALSE, warning=FALSE, include=TRUE}
 F_mat_fem <- Female_parity_fert_list_UK
-F_mat_male <- Male_parity_fert_list_UK
+F_mat_male <- Female_parity_fert_list_UK
 T_mat_fem <- Parity_transfers_by_age_list_UK
 T_mat_male <- Parity_transfers_by_age_list_UK
 U_mat_fem <- Female_parity_mortality_list_UK
@@ -102,7 +95,7 @@ List starting 1965 ending 2022.
 
 F_mat_male == F_mat_fem.
 
-T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities 
+T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities
 a female moves up parity (inner list has length of number of age-classes).
 Outer list starting 1965 ending 2022
 
@@ -113,28 +106,30 @@ H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0
 ### 1. Accumulated number of kin Focal expects over the lifecourse under time-varying rates from 1965 to 2005 ###
 
 We feed the above inputs into the matrix model, along with other arguments:
-UK sex ratio --> `birth_female` = 0.49
-We are considering parity --> `parity` = TRUE
-We want all of Focal's kin network --> `output_kin` = FALSE
-Accumulated kin in this example --> `summary_kin` = TRUE
-Focal is female --> `sex_Focal` = "Female"
-Focal born into parity 0 --> `initial_stage_Focal` = 1
-timescale from 1965-1985 -- > `output_years` = seq(1965, 1965 + 40)
-
-Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage 
-distribution of kin. 
-
-Notice that the timescale argument `output_years` = seq(1965,2005) gives a sequence of 1965, 1966, ..., 2004, 2005 of length 41. 
-The first sets of time-varying vital rates in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965), 
+
+-   UK sex ratio --> `birth_female` = 0.49
+-   We are considering parity --> `parity` = TRUE
+-   We want some of Focal's kin network --> `output_kin` = c("d", "oa", "ys", "os")
+-   Accumulated kin in this example --> `summary_kin` = TRUE
+-   Focal is female --> `sex_Focal` = "Female"
+-   Focal born into parity 0 --> `initial_stage_Focal` = 1
+-   timescale as ouptut -- > `output_years` = c(1965, 1975, 1985, 1995, 2005)
+
+Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage
+distribution of kin.
+
+The first sets of time-varying vital rates in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965),
 the 41-st entry is U_mat_fem[[(1+40)]] (mortality in 2005). We require consistency between the length of the list of vital rates
 and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005). Therefore we use the input lists of demographic rates
 
-`U_list_females` = U_mat_fem[1:(1+no_years)] which runs from U_mat_fem[[1]] = 1965 set of rates, up to U_mat_fem[[41]] = 2005 set of rates, 
+`U_list_females` = U_mat_fem[1:(1+no_years)] which runs from U_mat_fem[[1]] = 1965 set of rates, up to U_mat_fem[[41]] = 2005 set of rates,
 and so on...
 
+> this run takes some time (round 10 min) so we don´t include the output in the vignette. Please try it!
+
 ```{r, message=FALSE, warning=FALSE}
 # Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
-no_years <- 40
+no_years <- 5
 # and we start projecting kin in 1965
 # We decide here to count accumulated kin by age of Focal, and not distributions of kin
 kin_out_1965_2005 <-
@@ -147,21 +142,24 @@ kin_out_1965_2005 <-
                                     H_list = H_mat[1:(1+no_years)],
                                     birth_female = 1 - 0.51, ## Sex ratio -- UK value
                                     parity = TRUE,
-                                    output_kin = FALSE,
+                                    output_kin = c("d", "oa", "ys", "os"),
                                     summary_kin = TRUE,
                                     sex_Focal = "Female", ##  define Focal's sex at birth
                                     initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
-  )
+                                    output_years = c(1965, 1975, 1985, 1995, 2005), ## the sequence of years we run the function over
+                                    age_year_consitent = TRUE, ## we use 5-year age classes
+                                    age_increment = NULL
+)
+
 ```
 ### 1.1. Visualizing the output ###
 
 ```{r, message=FALSE, warning=FALSE}
 
-head(kin_out_1965_2005)
+head(kin_out_1965_2005$kin_summary)
 ```
 
-Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin, 
+Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin,
 and produce the marginal stage distribution given age of Focal. We have a column corresponding to sex of kin `sex_kin`,
 a column showing which `year` we are considering, and a column headed `group` which selects the kin type.
 Finally, we have columns showing Focal's cohort of birth `cohort` (e.g., year - age of Focal), and an as.factor() equivalent.
@@ -169,19 +167,18 @@ Finally, we have columns showing Focal's cohort of birth `cohort` (e.g., year -
 
 ### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
 
-Let's suppose that we really want to understand the age*parity distributions of the accumulated number 
-of aunts and uncles older than Focal's mother and father, for each age of Focal, over years 1965, 1975, 1985, 1995, 2005. 
-Some people will do.... 
+Let's suppose that we really want to understand the age*parity distributions of the accumulated number
+of aunts and uncles older than Focal's mother and father, for each age of Focal, over years 1965, 1975, 1985, 1995, 2005.
+Some people will do....
 
 We restrict Focal's kinship network to aunts and uncles older than Focal's mother by `group` == "oa". We visualise the marginal
-parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different colour schemes. Implicit in the 
-below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, 
+parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different colour schemes. Implicit in the
+below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955,
 while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
-kin_out_1965_2005 %>%
-  dplyr::filter(group == "oa",
-                year %in% c(1965, 1975, 1985, 1995, 2005)) %>%
+kin_out_1965_2005$kin_summary %>%
+  dplyr::filter(group == "oa") %>%
   ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
   ggplot2::facet_grid(sex_kin ~ year) +
@@ -193,9 +190,8 @@ kin_out_1965_2005 %>%
 We could also consider any other kin in Focal's network, for instance, offspring using `group` == "d"
 
 ```{r, fig.height=6, fig.width=8}
-kin_out_1965_2005 %>%
-  dplyr::filter(group == "d",
-                year %in% c(1965, 1975, 1985, 1995, 2005)) %>%
+kin_out_1965_2005$kin_summary %>%
+  dplyr::filter(group == "d") %>%
   ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
   ggplot2::facet_grid(sex_kin ~ year) +
@@ -206,11 +202,11 @@ kin_out_1965_2005 %>%
 ```
 ### 1.1.2. Plotting the kin of Focal as a function of Focal's cohort of birth ####
 
-Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific. 
+Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific.
 We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring and only show Focals born of `cohort` 1910, 1925, or 1965:
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005 %>%
+kin_out_1965_2005$kin_summary %>%
   dplyr::filter(group == "d", cohort %in% c(1910,1925,1965) ) %>%
   ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
@@ -221,42 +217,20 @@ kin_out_1965_2005 %>%
 ```
 The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
 
-The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by 
+The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by
 a well mixed parity-distribution at this age of Focal.
 
 the RHS plot (1965 cohort) simply reflects the fact that Focal will not start reproduction until around 15 years old.
 
 ### 2. Now lets consider the distributions of kin Focal expects over the lifecourse ###
 
-To obtain distributions of kin as output, we simply change the function argument: `summary_kin` = FALSE
+To obtain distributions of kin as output, we simply use the `kin_full` data.frame.
 
-```{r, message=FALSE, warning=FALSE}
-rm(kin_out_1965_2005)
-gc()
-no_years <- 40
-
-kin_out_1965_2005_full <-
-  kin_multi_stage_time_variant_2sex(U_mat_fem[1:(1+no_years)],
-                                    U_mat_male[1:(1+no_years)],
-                                    F_mat_fem[1:(1+no_years)],
-                                    F_mat_male[1:(1+no_years)],
-                                    T_mat_fem[1:(1+no_years)],
-                                    T_mat_fem[1:(1+no_years)],
-                                    H_mat[1:(1+no_years)],
-                                    birth_female = 1 - 0.51, ## Sex ratio -- UK value
-                                    parity = TRUE,
-                                    output_kin = FALSE,
-                                    summary_kin = FALSE,
-                                    sex_Focal = "Female", ##  define Focal's sex at birth
-                                    initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    output_years = seq(1965, (1965 + no_years)) ## the sequence of years we run the function over
-)
-```
 ### 2.1. Visualizing the output ###
 
 ```{r, message=FALSE, warning=FALSE}
 
-head(kin_out_1965_2005_full)
+head(kin_out_1965_2005$kin_full)
 ```
 
 Notice the additional column `age_kin`. Rather than grouping kin by stage and summing over all ages,
@@ -270,9 +244,8 @@ Restricting ourselves to the years 1965, 1975, 1985, 1995, 2005, we can plot the
 of these kin over the considered periods, as shown below:
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005_full %>%
+kin_out_1965_2005$kin_full %>%
   dplyr::filter(group == "ys",
-                year %in% c(1965, 1975, 1985, 1995, 2005),
                 age_focal == 50) %>%
   ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
@@ -284,14 +257,13 @@ kin_out_1965_2005_full %>%
   ggplot2::ggtitle("Focal 50")
 ```
 
-Notice the discontinuity along the x-abissca at 50. This reflects the fact that Focal's younger siblings 
+Notice the discontinuity along the x-abscissa at 50. This reflects the fact that Focal's younger siblings
 cannot are of age <50. Contrastingly, when we look at the age*stage distribution of older siblings, we observe another
 discontinuity which bounds kin to be of age >50, as plotted below:
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005_full %>%
+kin_out_1965_2005$kin_full %>%
   dplyr::filter(group == "os",
-                year %in% c(1965, 1975, 1985, 1995, 2005),
                 age_focal == 50) %>%
   ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
   ggplot2::geom_bar(position = "stack", stat = "identity") +
@@ -306,9 +278,8 @@ kin_out_1965_2005_full %>%
 With a simple bit of playing with the output data frame, we can plot the age*stage distribution of the combined siblings of Focal
 
 ```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005_full %>%
+kin_out_1965_2005$kin_full %>%
   dplyr::filter((group == "ys" | group == "os"),
-                year %in% c(1965, 1975, 1985, 1995, 2005),
                 age_focal == 50) %>%
   tidyr::pivot_wider(names_from = group, values_from = count) %>%
   dplyr::mutate(count = `ys` + `os`) %>%

From 3b2e90a38cf0851f20307e843479bf79c7d10d05 Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Fri, 21 Feb 2025 11:55:54 +0000
Subject: [PATCH 61/89] age year

---
 R/kin_multi_stage_time_variant_2sex.R                 | 6 +++---
 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index e1bc8ff..e7506ee 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -34,7 +34,7 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                               H_list = NULL,
                                               birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
                                               parity = FALSE,
-                                              output_kin = FALSE, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
+                                              output_kin = NULL, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
                                               summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
                                               sex_Focal = "Female", # Female Focal is default
                                               initial_stage_Focal = NULL,
@@ -757,7 +757,7 @@ create_cumsum_df <- function(kin_matrix_lists,
   df_year_list <- do.call("rbind", df_year_list)
   df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
                                                  cohort_factor = as.factor(cohort))
-  if(specific_kin != FALSE){
+  if(!is.null(specific_kin)){
     df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
   }
   return(df_year_list)
@@ -828,7 +828,7 @@ create_full_dists_df <- function(kin_matrix_lists,
   df_year_list <- do.call("rbind", df_year_list)
   df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
                                                  cohort_factor = as.factor(cohort))
-  if(specific_kin != FALSE){
+  if(!is.null(specific_kin)){
     df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
   }
   return(df_year_list)
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 66872b0..897f77b 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -129,7 +129,7 @@ and so on...
 
 ```{r, message=FALSE, warning=FALSE}
 # Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
-no_years <- 5
+no_years <- 6
 # and we start projecting kin in 1965
 # We decide here to count accumulated kin by age of Focal, and not distributions of kin
 kin_out_1965_2005 <-
@@ -147,8 +147,8 @@ kin_out_1965_2005 <-
                                     sex_Focal = "Female", ##  define Focal's sex at birth
                                     initial_stage_Focal = 1, ## Define Focal's stage at birth
                                     output_years = c(1965, 1975, 1985, 1995, 2005), ## the sequence of years we run the function over
-                                    age_year_consitent = TRUE, ## we use 5-year age classes
-                                    age_increment = NULL
+                                    age_year_consitent = FALSE, ## we use 5-year age classes
+                                    age_increment = 1
 )
 
 ```

From f96f8a56c1d5cb70859f326a7e08478c0a56166d Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Fri, 21 Feb 2025 15:24:05 +0000
Subject: [PATCH 62/89] fixing time and age inc

---
 R/kin_multi_stage_time_variant_2sex.R         | 32 +++++++++++++------
 ...eference_TwoSex_MultiState_TimeVariant.Rmd |  7 ++--
 2 files changed, 26 insertions(+), 13 deletions(-)

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index e7506ee..baa2aa1 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -20,7 +20,10 @@
 #' @param sex_Focal character. Female or Male as the user requests.
 #' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
 #' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
-#'
+#' @param model_years. The full timescale on which we run the matrix model. From these periods we extract the ``output_years''.
+#'                     Note that if we use abridged life-tables: e.g., 1960,1965,1970 to run the model, by default age_increment = 5
+#' @param age_year_consistent logical. Null sets age-bridge to be equal to year
+#' @param age_increment. numeric. If age_year_consisent FALSE set own age-gap
 #' @return A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
 
 #' @export
@@ -39,15 +42,17 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                               sex_Focal = "Female", # Female Focal is default
                                               initial_stage_Focal = NULL,
                                               output_years,
+                                              model_years,
                                               age_year_consitent = TRUE,
                                               age_increment = NULL){
 
-  no_years <- length(U_list_females)
+  no_years <- (-1+length(U_list_females))
   na <- nrow(U_list_females[[1]])
   ns <- ncol(U_list_females[[1]])
-  if(age_year_consitent){age_increment <- as.numeric(output_years[2]-output_years[1])}
+  if(age_year_consitent){age_increment <- as.numeric(model_years[2]-model_years[1])}
   # Ensure inputs are lists of matrices and that the timescale same length
-  if(length(U_list_females) < (length(output_years))){stop("Proposed timescale longer than demographic timescale")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
+  if(length(U_list_females) != (length(model_years))){stop("Proposed timescale longer than demographic timescale")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
+  if(output_years[length(output_years)] > model_years[length(model_years)]){stop("Output years longer than model run")}
   if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
@@ -80,6 +85,7 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
     total = no_years + 1, clear = FALSE, width = 60)
   tictoc::tic()
   for(year in 1:no_years){
+
     pb$tick()
     T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
     T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
@@ -265,7 +271,7 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
   kin_full <- create_full_dists_df(relative_data,
                                    relative_names,
                                    output_years,
-                                   output_years[1],
+                                   model_years,
                                    na,
                                    ns,
                                    output_kin,
@@ -274,7 +280,7 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
     kin_summary <- create_cumsum_df(relative_data,
                                     relative_names,
                                     output_years,
-                                    output_years[1],
+                                    model_years,
                                     na,
                                     ns,
                                     output_kin,
@@ -707,16 +713,19 @@ all_kin_dy_TV <- function(Uf,
 create_cumsum_df <- function(kin_matrix_lists,
                              kin_names,
                              years,
-                             start_year,
+                             model_years,
                              na,
                              ns,
                              specific_kin = NULL,
                              increment = NULL){
   if(length(years) > length(kin_matrix_lists[[1]])){stop("More years than data")}
+
+  matrix_model_time <- model_years
+
   age_inc <- increment
   df_year_list <- list()
   for(j in years){
-    ii <- which(years == j)
+    ii <- which(matrix_model_time == j)
     df_list <- list()
     for(i in 1 : length(kin_names)){
       kin_member <- kin_names[[i]]
@@ -779,17 +788,20 @@ create_cumsum_df <- function(kin_matrix_lists,
 create_full_dists_df <- function(kin_matrix_lists,
                                  kin_names,
                                  years,
-                                 start_year,
+                                 model_years,
                                  na,
                                  ns,
                                  specific_kin = NULL,
                                  increment = NULL){
   if(length(years) > length(kin_matrix_lists[[1]])){stop("More years than data")}
+
+  matrix_model_time <- model_years
+
   age_inc <- increment
   df_year_list <- list()
   for(j in years){
     df_list <- list()
-    ii <- which(years == j)
+    ii <- which(matrix_model_time == j)
     for(i in 1 : length(kin_names)){
       kin_member <- kin_names[[i]]
       kin_data <- kin_matrix_lists[[i]]
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 897f77b..d3fb039 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -129,7 +129,7 @@ and so on...
 
 ```{r, message=FALSE, warning=FALSE}
 # Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
-no_years <- 6
+no_years <- 40
 # and we start projecting kin in 1965
 # We decide here to count accumulated kin by age of Focal, and not distributions of kin
 kin_out_1965_2005 <-
@@ -147,8 +147,9 @@ kin_out_1965_2005 <-
                                     sex_Focal = "Female", ##  define Focal's sex at birth
                                     initial_stage_Focal = 1, ## Define Focal's stage at birth
                                     output_years = c(1965, 1975, 1985, 1995, 2005), ## the sequence of years we run the function over
-                                    age_year_consitent = FALSE, ## we use 5-year age classes
-                                    age_increment = 1
+                                    model_years <- seq(1965, 2005),
+                                    age_year_consitent = TRUE, ## we use 5-year age classes
+                                    age_increment = NULL
 )
 
 ```

From 7e98a866f9d93fff62d3a9392137b8e8267094d0 Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Fri, 21 Feb 2025 15:36:50 +0000
Subject: [PATCH 63/89] removing old

---
 R/kin_multi_stage_time_variant_2sex_preJANG.R | 1048 -----------------
 1 file changed, 1048 deletions(-)
 delete mode 100644 R/kin_multi_stage_time_variant_2sex_preJANG.R

diff --git a/R/kin_multi_stage_time_variant_2sex_preJANG.R b/R/kin_multi_stage_time_variant_2sex_preJANG.R
deleted file mode 100644
index ad413e6..0000000
--- a/R/kin_multi_stage_time_variant_2sex_preJANG.R
+++ /dev/null
@@ -1,1048 +0,0 @@
-
-
-#' Estimate kin counts by age, stage, and sex, in a time variant framework
-
-#' @description Implementation of combined formal demographic models: Caswell II,III,IV.
-
-#' @param U_list_females list with matrix entries: period-specific female survival probabilities. Age in rows and states in columns.
-#' @param U_list_males list with matrix entries: period-specific male survival probabilities. Age in rows and states in columns.
-#' @param F_list_females list with matrix with elements: period-specific female fertility (age in rows and states in columns).
-#' @param F_list_males list with matrix entries: period-specific male fertility (age in rows and states in columns).
-#' @param T_list_females list of lists with matrix entries: each outer list entry is period-specific, and composed of
-#'                     a list of stochastic matrices which describe age-specific female probabilities of transferring stage
-#' @param T_list_males list of lists with matrix entries: each outer list entry is period-specific, and composed of
-#'                     a list of stochastic matrices which describe age-specific male probabilities of transferring stage
-#' @param H_list list with matrix entries: redistribution of newborns across each stage to a specific age-class
-#' @param birth_female numeric. birth ratio of females to males in population
-#' @param parity logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default `TRUE`.
-#' @param output_kin vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.
-#' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if TRUE, and kin by their age*stage distribution by age of Focal if FALSE.
-#' @param sex_Focal character. Female or Male as the user requests.
-#' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
-#' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
-#'
-#' @return A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
-
-#' @export
-#'
-kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
-                                              U_list_males = NULL,
-                                              F_list_females = NULL,
-                                              F_list_males = NULL,
-                                              T_list_females = NULL,
-                                              T_list_males = NULL,
-                                              H_list = NULL,
-                                              birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
-                                              parity = FALSE,
-                                              output_kin = FALSE, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
-                                              summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
-                                              sex_Focal = "Female", # Female Focal is default
-                                              initial_stage_Focal = NULL,
-                                              output_years){
-
-  no_years <- length(U_list_females)
-  na <- nrow(U_list_females[[1]])
-  ns <- ncol(U_list_females[[1]])
-
-  # Ensure inputs are lists of matrices and that the timescale same length
-  if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
-  if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
-  if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
-  if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
-
-  ### Define empty lists for the accumulated kin of Focals's life-course -- each list entry will reflect a time-period
-  changing_pop_struct <- list()
-  Focal_array <- list()
-  mom_array <- list()
-  gran_array <- list()
-  great_gran_array <- list()
-  daughter_array <- list()
-  younger_sis_array <- list()
-  grand_daughter_array <-list()
-  great_grand_daughter_array <- list()
-  older_sister_array <- list()
-  younger_aunt_array <- list()
-  older_aunt_array <- list()
-  younger_niece_array <- list()
-  older_niece_array <- list()
-  younger_cousin_array <- list()
-  older_cousin_array <- list()
-
-  ### At each time-period we: 1) -- construct the time-variant projection matrices:
-  ###                               U_tilde : transfers across stage and advances age
-  ###                               F_tilde : makes newborns from stage/age; puts them to stage/age
-  ###                         2) -- project Focal and kin using above projection matrices
-
-  pb <- progress::progress_bar$new(
-    format = "Timescale [:bar] :percent",
-    total = no_years + 1, clear = FALSE, width = 60)
-  tictoc::tic()
-  for(year in 1:no_years){
-    pb$tick()
-    T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
-    T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
-    T_f_list <- list()
-    T_m_list <- list()
-    F_f_list <- list()
-    F_m_list <- list()
-    U_f_list <- list()
-    U_m_list <- list()
-    H_list2 <- list()
-
-    for(stage in 1:ns){
-      Uf <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
-      Matrix::diag(Uf[-1,-ncol(Uf)]) <- U_list_females[[year]][1:(na-1),stage]
-      Uf[na,na] <- U_list_females[[year]][na,stage]
-      Um <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
-      Matrix::diag(Um[-1,-ncol(Um)]) <- U_list_males[[year]][1:(na-1),stage]
-      Um[na,na] <- U_list_males[[year]][na,stage]
-      U_f_list[[(1+length(U_f_list))]] <- Uf
-      U_m_list[[(1+length(U_m_list))]] <- Um
-      H_mat <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
-      H_mat[1,] <- 1
-      H_list2[[(1+length(H_list2))]] <- H_mat
-    }
-    for(age in 1:na){
-      T_f <- T_data_f[[age]]
-      T_m <- T_data_m[[age]]
-      T_f_list[[(1+length(T_f_list))]] <- T_f
-      T_m_list[[(1+length(T_m_list))]] <- T_m
-      F_f <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
-      F_m <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
-      F_f[1,] <- F_list_females[[year]][age,]
-      F_m[1,] <- F_list_males[[year]][age,]
-      F_f_list[[(1+length(F_f_list))]] <- F_f
-      F_m_list[[(1+length(F_m_list))]] <- F_m
-    }
-    ## create the appropriate block-diagonal matrices
-    U_f_BDD <- block_diag_function(U_f_list) ## direct sum of female survivorship, independent over stage (ns diagonal blocks)
-    U_m_BDD <- block_diag_function(U_m_list) ## direct sum of male survivorship, independent over stage (ns diagonal blocks)
-    H_BDD <- block_diag_function(H_list2) ## direct sum of which age newborns enter, independent over stage (ns diagonal blocks)
-    T_f_BDD <- block_diag_function(T_f_list) ## direct sum of female stage transitions, independent over age (na diagonal blocks)
-    T_m_BDD <- block_diag_function(T_m_list) ## direct sum of male stage transitions, independent over age (na diagonal blocks)
-    F_f_BDD <- block_diag_function(F_f_list) ## direct sum of female stage->stage reproductions, independent over age (na blocks)
-    F_m_BDD <- block_diag_function(F_m_list) ## direct sum of male stage->stage reproductions, independent over age (na blocks)
-
-    ## create the appropriate projection matrices
-    U_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
-      U_f_BDD %*%
-      K_perm_mat(ns, na) %*%
-      T_f_BDD
-
-    ## create sex-specific age*stage projections
-    U_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
-      U_m_BDD %*%
-      K_perm_mat(ns, na) %*%
-      T_m_BDD
-
-    F_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
-      H_BDD %*%
-      K_perm_mat(ns, na) %*%
-      F_f_BDD
-
-    F_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
-      H_BDD %*%
-      K_perm_mat(ns, na) %*%
-      F_m_BDD
-
-    ## if year == 1 we are at the boundary condition t=0 apply time-invariant kinship projections
-    if(year == 1){
-      ## Output of the static model
-      kin_out_1 <- all_kin_dy(U_tilde_females,
-                              U_tilde_males ,
-                              F_tilde_females,
-                              F_tilde_males,
-                              1-birth_female,
-                              na,
-                              ns,
-                              parity,
-                              sex_Focal,
-                              initial_stage_Focal)
-      ### Relative lists' first entries
-      Focal_array[[(1+length(Focal_array))]] <- kin_out_1[["Focal"]]
-      daughter_array[[(1+length(daughter_array))]] <- kin_out_1[["d"]]
-      grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out_1[["gd"]]
-      great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out_1[["ggd"]]
-      mom_array[[(1+length(mom_array))]] <- kin_out_1[["m"]]
-      gran_array[[(1+length(gran_array))]] <- kin_out_1[["gm"]]
-      great_gran_array[[(1+length(great_gran_array))]] <- kin_out_1[["ggm"]]
-      younger_sis_array[[( 1+length(younger_sis_array))]] <- kin_out_1[["ys"]]
-      older_sister_array[[(1+length(older_sister_array))]] <- kin_out_1[["os"]]
-      younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out_1[["ya"]]
-      older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out_1[["oa"]]
-      younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out_1[["nys"]]
-      older_niece_array[[(1+length(older_niece_array))]] <- kin_out_1[["nos"]]
-      younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out_1[["cya"]]
-      older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out_1[["coa"]]
-      changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out_1[["ps"]]
-
-    }
-    updating_Focal <- Focal_array[[year]]
-    updating_daughter <- daughter_array[[year]]
-    updating_grand_daughter <- grand_daughter_array[[year]]
-    updating_great_grand_daughter <- great_grand_daughter_array[[year]]
-    updating_mom <- mom_array[[year]]
-    updating_gran <- gran_array[[year]]
-    updating_great_gran <- great_gran_array[[year]]
-    updating_younger_sis <- younger_sis_array[[year]]
-    updating_older_sis <- older_sister_array[[year]]
-    updating_youner_aunt <- younger_aunt_array[[year]]
-    updating_older_aunt <- older_aunt_array[[year]]
-    updating_younger_niece <- younger_niece_array[[year]]
-    updating_older_niece <- older_niece_array[[year]]
-    updating_younger_cousin <- younger_cousin_array[[year]]
-    updating_older_cousin <- older_cousin_array[[year]]
-    updating_pop_struct <- changing_pop_struct[[year]]
-
-    ## Output of the time-variant model
-    kin_out <- all_kin_dy_TV(U_tilde_females,
-                             U_tilde_males,
-                             F_tilde_females,
-                             F_tilde_males,
-                             1-birth_female,
-                             na,
-                             ns,
-                             parity,
-                             sex_Focal,
-                             initial_stage_Focal,
-                             updating_Focal,
-                             updating_daughter,
-                             updating_grand_daughter,
-                             updating_great_grand_daughter,
-                             updating_mom,
-                             updating_gran,
-                             updating_great_gran,
-                             updating_older_sis,
-                             updating_younger_sis,
-                             updating_older_niece,
-                             updating_younger_niece,
-                             updating_older_aunt,
-                             updating_youner_aunt,
-                             updating_older_cousin,
-                             updating_younger_cousin,
-                             updating_pop_struct)
-    ## Relative lists entries correspond to timescale periods (each entry an kin age*stage*2 by Focal age matrix)
-    Focal_array[[(1+length(Focal_array))]] <- kin_out[["Focal"]]
-    daughter_array[[(1+length(daughter_array))]] <- kin_out[["d"]]
-    grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out[["gd"]]
-    great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out[["ggd"]]
-    mom_array[[(1+length(mom_array))]] <- kin_out[["m"]]
-    gran_array[[(1+length(gran_array))]] <- kin_out[["gm"]]
-    great_gran_array[[(1+length(great_gran_array))]] <- kin_out[["ggm"]]
-    younger_sis_array[[(1+length(younger_sis_array))]] <- kin_out[["ys"]]
-    older_sister_array[[(1+length(older_sister_array))]] <- kin_out[["os"]]
-    younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out[["ya"]]
-    older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out[["oa"]]
-    younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out[["nys"]]
-    older_niece_array[[(1+length(older_niece_array))]] <- kin_out[["nos"]]
-    younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out[["cya"]]
-    older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out[["coa"]]
-    changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out[["ps"]]
-  }
-  tictoc::toc()
-  ## create a list of output kin -- each element a time-period specific list of matrices
-  ## label the kin names to match DemoKin:
-  relative_data <- list("Focal" = Focal_array,
-                        "d" = daughter_array,
-                        "gd" = grand_daughter_array,
-                        "ggd" = great_grand_daughter_array,
-                        "m" = mom_array,
-                        "gm" = gran_array,
-                        "ggm" = great_gran_array,
-                        "ys" = younger_sis_array,
-                        "os" = older_sister_array,
-                        "ya" = younger_aunt_array,
-                        "oa" = older_aunt_array,
-                        "nys" = younger_niece_array,
-                        "nos" = older_niece_array,
-                        "cya" = younger_cousin_array,
-                        "coa" = older_cousin_array)
-
-  relative_names <- names(relative_data)
-  ## create a nice data frame output
-  if(summary_kin){
-    kin_out <- create_cumsum_df(relative_data,
-                                relative_names,
-                                output_years[1]:output_years[length(output_years)],
-                                output_years[1],
-                                na,
-                                ns,
-                                output_kin)}
-  else{
-    kin_out <- create_full_dists_df(relative_data,
-                                    relative_names,
-                                    output_years[1]:output_years[length(output_years)],
-                                    output_years[1],
-                                    na,
-                                    ns,
-                                    output_kin)}
-
-  return(kin_out)
-}
-
-
-#' Title time invariant two-sex multi-state kin projections
-#'
-#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
-#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
-#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
-#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
-#' @param alpha scalar. birth ratio (male:female)
-#' @param na scalar. number of ages.
-#' @param ns scalar. number of stages.
-#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
-#' @param sex_Focal logical. Female or Male
-#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
-#'
-#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
-#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
-#' yielding the age*stage distribution of kin for each age of Focal
-
-all_kin_dy <- function(Uf,
-                       Um,
-                       Ff,
-                       Fm,
-                       alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
-                       na, ## na = number of ages
-                       ns, ## ns = number of stages
-                       Parity,
-                       sex_Focal, ## binary "F" or "M"
-                       Initial_stage_Focal){
-
-  n <- nrow(Uf) ## number of ages * stages for each sex
-
-  ## Projection matrices:
-
-  ## Uproj is a block diagonal matrix of block-structured Age*Stage matrices; independently over sex transfers individuals across stage and up age
-  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
-  ## Fproj is a Sex-block-structured matrix of block-structured Age*Stage matrices where males and females BOTH reproduce (by stage)
-  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  Fproj[1:n, 1:n] <- (1-alpha)*Ff ## Ff is Age*Stage block structured giving rate at which females in age-stage produce individuals in age-stage
-  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
-  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm ## Fm is Age*Stage block structured giving rate at which males in age-stage produce individuals in age-stage
-  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
-
-  ## Fprojstar is a Sex-block-structured matrix of block-structured Age*Stage matrices where ONLY females reproduce
-  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
-  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
-  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
-
-  ## The stable population structure is an age*stage*sex vector:
-  ##                                                            1:n gives the female age*stage structure
-  ##                                                            (1+n):2n gives the male age*stage structure
-  population_age_stage_structure <- SD(Uproj + Fprojstar)
-
-  ### Stable distribution of mothers needs adjusting if we work with parity
-  if(Parity){
-    Initial_stage_Focal <- 1
-
-    population_age_stage_of_parenting <- pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-  else{
-    population_age_stage_of_parenting <- pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-
-  ####################################### The dynamics of Kinship, starting with Focal who is no longer a unit vector
-
-  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
-  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
-  m_t <- get_G(Um, na, ns)
-  G_tilde <- block_diag_function(list(f_t,m_t))
-  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  IC_Focal <- rep(0, 2*n)
-  if(sex_Focal == "Female"){
-    entry <- 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1}
-  else{
-    entry <- n + 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1
-  }
-
-  ### empty kin matrices for all of Focal's kin
-  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_aunt_uncle  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-
-
-  ### Initial distributions for kin with non-zero deterministic initial conditions:
-  # Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
-  X_Focal[,1] <- IC_Focal
-  X_parents[, 1] <- mothers_age_stage
-
-  ### projection all kin with deterministic initial conditions
-  for(i in 1 : (na-1)){
-    X_Focal[,i+1] <- G_tilde %*% X_Focal[,i]
-    X_parents[, i+1] <- Uproj %*% X_parents[, i]
-    X_younger_sibs[,i+1] <- Uproj %*% X_younger_sibs[,i] + Fprojstar %*% X_parents[,i]
-    X_younger_niece_nephew[,i+1] <- Uproj %*% X_younger_niece_nephew[,i] + Fproj %*% X_younger_sibs[,i]
-    X_children[,i+1] <- Uproj %*% X_children[,i] + Fproj %*% X_Focal[,i]
-    X_grand_children[,i+1] <- Uproj %*% X_grand_children[,i] + Fproj %*% X_children[,i]
-    X_great_grand_children[,i+1] <- Uproj %*% X_great_grand_children[,i] + Fproj %*% X_grand_children[,i]
-  }
-
-  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
-  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
-  IC_f_grand_pars <- mothers_age_dist
-  IC_m_grand_pars <- fathers_age_dist
-  IC_f_great_grand_pars <- mothers_age_dist
-  IC_m_great_grand_pars <- fathers_age_dist
-  IC_older_sibs_f <- mothers_age_dist
-  IC_younger_aunts_uncles_f <- mothers_age_dist
-  IC_younger_aunts_uncles_m <- fathers_age_dist
-  IC_older_niece_nephew_f <- mothers_age_dist
-  for(ic in 1 : (na)){
-    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*X_parents[,ic] ## IC the sum of parents of Focal's parents,
-    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*X_grand_parents[,ic]
-    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*X_children[,ic]
-    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*X_grand_children[,ic]
-    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*X_younger_sibs[,ic]
-  }
-
-  ### Projections of grand parenst, older sibs, younger aunts/uncles, older nieces/nephews
-  for(i in 1: (na-1)){
-    X_grand_parents[, i+1] <- Uproj %*% X_grand_parents[, i]
-    X_great_grand_parents[, i+1] <- Uproj %*% X_great_grand_parents[, i]
-    X_older_sibs[,i+1] <- Uproj %*% X_older_sibs[,i]
-    X_older_niece_nephew[,i+1] <- Uproj %*% X_older_niece_nephew[,i] + Fproj %*% X_older_sibs[,i]
-    X_younger_aunts_uncles[,i+1] <- Uproj %*% X_younger_aunts_uncles[,i] + Fprojstar %*% X_grand_parents[,i]
-  }
-
-  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
-  ## older unts/uncles, older cousins, younger cousins
-  IC_older_aunt_uncle_f <- mothers_age_dist
-  IC_older_aunt_uncle_m <- fathers_age_dist
-  IC_older_cousins_f <- mothers_age_dist
-  IC_older_cousins_m <- fathers_age_dist
-  IC_younger_cousins_f <- mothers_age_dist
-  IC_younger_cousins_m <- fathers_age_dist
-  for(ic in 1 : (na-1)){
-    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*X_older_sibs[,ic]
-    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*X_older_niece_nephew[,ic]
-    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*X_younger_niece_nephew[,ic]
-  }
-
-  ## Projections of older unts/uncles, older cousins, younger cousins
-  for(i in 1: (na-1)){
-    X_older_aunt_uncle[,i+1] <- Uproj %*% X_older_aunt_uncle[,i]
-    X_older_cousins[,i+1] <- Uproj %*% X_older_cousins[,i] + Fproj %*% X_older_aunt_uncle[,i]
-    X_younger_cousins[,i+1] <- Uproj %*% X_younger_cousins[,i] + Fproj %*% X_younger_aunts_uncles[,i]
-  }
-
-  #### OUTPUT of all kin
-  return(list("Focal" = X_Focal,
-              "d" = X_children,
-              "gd" = X_grand_children,
-              "ggd" = X_great_grand_children,
-              "m" = X_parents,
-              "gm" = X_grand_parents,
-              "ggm" = X_great_grand_parents,
-              "os" = X_older_sibs,
-              "ys" = X_younger_sibs,
-              "nos" = X_older_niece_nephew,
-              "nys" = X_younger_niece_nephew,
-              "oa" = X_older_aunt_uncle,
-              "ya" = X_younger_aunts_uncles,
-              "coa" = X_older_cousins,
-              "cya" = X_younger_cousins,
-              "ps" = population_age_stage_structure
-  ))
-}
-
-
-#' Title time-variant two-sex multi-state kin projections
-#'
-#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
-#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
-#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
-#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
-#' @param alpha scalar. birth ratio (male:female)
-#' @param na scalar. number of ages.
-#' @param ns scalar. number of stages.
-#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
-#' @param sex_Focal logical. Female or Male
-#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
-#' @param previous_kin_Focal matrix. last years kinship output.
-#' @param prev_kin_children matrix. last years kinship output.
-#' @param prev_kin_grandchildren matrix. last years kinship output.
-#' @param prev_kin_greatgrandchildren matrix. last years kinship output.
-#' @param prev_kin_parents matrix. last years kinship output.
-#' @param prev_kin_grand_parents matrix. last years kinship output.
-#' @param prev_kin_older_sibs matrix. last years kinship output.
-#' @param prev_kin_younger_sibs matrix. last years kinship output.
-#' @param prev_kin_older_niece_nephew matrix. last years kinship output.
-#' @param prev_kin_younger_niece_nephew matrix. last years kinship output.
-#' @param prev_kin_older_aunts_uncles matrix. last years kinship output.
-#' @param prev_kin_younger_aunts_uncles matrix. last years kinship output.
-#' @param prev_kin_older_cousins matrix. last years kinship output.
-#' @param prev_kin_younger_cousins matrix. last years kinship output.
-#' @param previous_population_age_stage_structure vector. The transient "population structure" (age*stage distributed)
-#'
-#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
-#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
-#' yielding the age*stage distribution of kin for each age of Focal
-#'
-all_kin_dy_TV <- function(Uf,
-                          Um,
-                          Ff,
-                          Fm,
-                          alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
-                          na, ## number of ages
-                          ns, ## number of stages
-                          Parity,
-                          sex_Focal,
-                          Initial_stage_Focal,
-                          previous_kin_Focal,
-                          prev_kin_children,
-                          prev_kin_grandchildren,
-                          prev_kin_greatgrandchildren,
-                          prev_kin_parents,
-                          prev_kin_grand_parents,
-                          prev_kin_great_grand_parents,
-                          prev_kin_older_sibs,
-                          prev_kin_younger_sibs,
-                          prev_kin_older_niece_nephew,
-                          prev_kin_younger_niece_nephew,
-                          prev_kin_older_aunts_uncles,
-                          prev_kin_younger_aunts_uncles,
-                          prev_kin_older_cousins,
-                          prev_kin_younger_cousins,
-                          previous_population_age_stage_structure){
-
-  n <- nrow(Uf)
-  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
-  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  Fproj[1:n, 1:n] <- (1-alpha)*Ff
-  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
-  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm
-  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
-  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
-  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
-  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
-
-  population_age_stage_structure <- previous_population_age_stage_structure
-  population_age_stage_structure <- population_age_stage_structure/sum(population_age_stage_structure)
-  population_age_stage_structure_next <- (Uproj + Fprojstar)%*%population_age_stage_structure
-
-  ### Stable distribution of mothers needs adjusting if we work with parity
-  if(Parity){
-    Initial_stage_Focal <- 1
-
-    population_age_stage_of_parenting <- pi_mix_TV_parity(Ff, Fm, alpha, na, ns, population_age_stage_structure)
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-  else{
-
-    population_age_stage_of_parenting <- pi_mix_TV(Ff, Fm, alpha, na, ns, population_age_stage_structure)
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-
-  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
-  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
-  m_t <- get_G(Um, na, ns)
-  G_tilde <- block_diag_function(list(f_t,m_t))
-  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  IC_Focal <- rep(0, 2*n)
-  if(sex_Focal == "Female"){
-    entry <- 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1}
-  else{
-    entry <- n + 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1
-  }
-
-  ### empty kin matrices for all of Focal's kin
-  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_aunt_uncle <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-
-  ### Initial distributions for kin with non-zero deterministic initial conditions:
-  ## Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
-  X_Focal[,1] <- IC_Focal
-  X_parents[, 1] <- mothers_age_stage
-  ### projection all above kin with deterministic initial conditions
-  for(i in 1 : (na-1)){
-    X_Focal[,i+1] <- G_tilde %*% previous_kin_Focal[,i]
-    X_parents[, i+1] <- Uproj %*% prev_kin_parents[, i]
-    X_younger_sibs[,i+1] <- Uproj %*% prev_kin_younger_sibs[,i] + Fprojstar %*% prev_kin_parents[,i]
-    X_younger_niece_nephew[,i+1] <- Uproj %*% prev_kin_younger_niece_nephew[,i] + Fproj %*% prev_kin_younger_sibs[,i]
-    X_children[,i+1] <- Uproj %*% prev_kin_children[,i] + Fproj %*% previous_kin_Focal[,i]
-    X_grand_children[,i+1] <- Uproj %*% prev_kin_grandchildren[,i] + Fproj %*% prev_kin_children[,i]
-    X_great_grand_children[,i+1] <- Uproj %*% prev_kin_greatgrandchildren[,i] + Fproj %*% prev_kin_grandchildren[,i]
-  }
-
-  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
-  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
-  IC_f_grand_pars <- mothers_age_dist
-  IC_m_grand_pars <- fathers_age_dist
-  IC_f_great_grand_pars <- mothers_age_dist
-  IC_m_great_grand_pars <- fathers_age_dist
-  IC_younger_aunts_uncles_f <- mothers_age_dist
-  IC_younger_aunts_uncles_m <- fathers_age_dist
-  IC_older_sibs_f <- mothers_age_dist
-  IC_older_niece_nephew_f <- mothers_age_dist
-  for(ic in 1 : (na)){
-    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*prev_kin_parents[,ic] ## IC the sum of parents of Focal's parents,
-    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*prev_kin_grand_parents[,ic]
-    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*prev_kin_children[,ic]
-    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*prev_kin_grandchildren[,ic]
-    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*prev_kin_younger_sibs[,ic]
-  }
-
-  ### Projections of older sibs, younger aunts/uncles, older nieces/nephews
-  for(i in 1: (na-1)){
-    X_grand_parents[, i+1] <- Uproj %*% prev_kin_grand_parents[, i]
-    X_great_grand_parents[, i+1] <- Uproj %*% prev_kin_great_grand_parents[, i]
-    X_older_sibs[,i+1] <- Uproj %*% prev_kin_older_sibs[,i]
-    X_older_niece_nephew[,i+1] <- Uproj %*% prev_kin_older_niece_nephew[,i] + Fproj %*% prev_kin_older_sibs[,i]
-    X_younger_aunts_uncles[,i+1] <- Uproj %*% prev_kin_younger_aunts_uncles[,i] + Fprojstar %*% prev_kin_grand_parents[,i]
-  }
-
-  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
-  ## older unts/uncles, older cousins, younger cousins
-  IC_older_aunt_uncle_f <- mothers_age_dist
-  IC_older_aunt_uncle_m <- fathers_age_dist
-  IC_older_cousins_f <- mothers_age_dist
-  IC_older_cousins_m <- fathers_age_dist
-  IC_younger_cousins_f <- mothers_age_dist
-  IC_younger_cousins_m <- fathers_age_dist
-  for(ic in 1 : (na-1)){
-    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*prev_kin_older_sibs[,ic]
-    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*prev_kin_older_niece_nephew[,ic]
-    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*prev_kin_younger_niece_nephew[,ic]
-  }
-
-  ## Projections of older unts/uncles, older cousins, younger cousins
-  for(i in 1: (na-1)){
-    X_older_aunt_uncle[,i+1] <- Uproj %*% prev_kin_older_aunts_uncles[,i]
-    X_older_cousins[,i+1] <- Uproj %*% prev_kin_older_cousins[,i] + Fproj %*% prev_kin_older_aunts_uncles[,i]
-    X_younger_cousins[,i+1] <- Uproj %*% prev_kin_younger_cousins[,i] + Fproj %*% prev_kin_younger_aunts_uncles[,i]
-  }
-
-  return(list("Focal" = X_Focal,
-              "d" = X_children,
-              "gd" = X_grand_children,
-              "ggd" = X_great_grand_children,
-              "m" = X_parents,
-              "gm" = X_grand_parents,
-              "ggm" = X_great_grand_parents,
-              "os" = X_older_sibs,
-              "ys" = X_younger_sibs,
-              "nos" = X_older_niece_nephew,
-              "nys" = X_younger_niece_nephew,
-              "oa" = X_older_aunt_uncle,
-              "ya" = X_younger_aunts_uncles,
-              "coa" = X_older_cousins,
-              "cya" = X_younger_cousins,
-              "ps" = population_age_stage_structure_next))
-}
-
-################## Create data frame output
-
-## Use of "pipe" (don't understand the name, but hey)
-`%>%` <- magrittr::`%>%`
-
-#' Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
-#'
-#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
-#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
-#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
-#' @param years vector. The timescale on which we implement the kinship model.
-#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
-#' @param na numeric. Number of ages.
-#' @param ns numeric. Number of stages.
-#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
-#'
-#' @return A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
-#'
-create_cumsum_df <- function(kin_matrix_lists,
-                             kin_names,
-                             years,
-                             start_year,
-                             na,
-                             ns,
-                             specific_kin = NULL){
-  df_year_list <- list()
-  for(j in years){
-    ii <- as.numeric(j) - start_year + 1
-    df_list <- list()
-    for(i in 1 : length(kin_names)){
-      kin_member <- kin_names[[i]]
-      kin_data <- kin_matrix_lists[[i]]
-      kin_data <- kin_data[[ii]]
-      df <- as.data.frame(as.matrix(kin_data))
-      dims <- dim( kin_data)
-      nr <- dims[1]
-      nc <- dims[2]
-      female_kin <- df[1:(nr/2), 1:nc]
-      male_kin <- df[ (1+nr/2) : nr, 1:nc]
-      female_kin$stage <- rep(seq(1, ns), na)
-      male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1)), each = ns)
-      male_kin$age <- rep(seq(0, (na-1)), each = ns)
-      female_kin$Sex <- "Female"
-      male_kin$Sex <- "Male"
-      both_kin <- rbind(female_kin, male_kin)
-      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
-        dplyr::group_by(variable, stage, Sex) %>%
-        dplyr::summarise(num = sum(value)) %>%
-        dplyr::ungroup()
-      both_kin <- both_kin %>% dplyr::transmute(age_focal = variable,
-                                                stage_kin = as.factor(stage),
-                                                count = num,
-                                                sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal)) - 1
-      df <- both_kin
-      df$year <- j
-      df$group <- kin_member
-      df_list[[length(df_list)+1]] <- df
-    }
-    df_list <- do.call("rbind", df_list)
-    df_year_list[[(1+length(df_year_list))]] <- df_list
-  }
-  df_year_list <- do.call("rbind", df_year_list)
-  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
-                                                 cohort_factor = as.factor(cohort))
-  if(specific_kin != FALSE){
-    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
-  }
-  return(df_year_list)
-}
-
-#' Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
-#'
-#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
-#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
-#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
-#' @param years vector. The timescale on which we implement the kinship model.
-#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
-#' @param na numeric. Number of ages.
-#' @param ns numeric. Number of stages.
-#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
-#'
-#' @return A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
-#'
-create_full_dists_df <- function(kin_matrix_lists,
-                                 kin_names,
-                                 years,
-                                 start_year,
-                                 na,
-                                 ns,
-                                 specific_kin = NULL){
-  df_year_list <- list()
-  for(j in years){
-    ii <- as.numeric(j) - start_year + 1
-    df_list <- list()
-    for(i in 1 : length(kin_names)){
-      kin_member <- kin_names[[i]]
-      kin_data <- kin_matrix_lists[[i]]
-      kin_data <- kin_data[[ii]]
-      df <- as.data.frame(as.matrix(kin_data))
-      dims <- dim( kin_data)
-      nr <- dims[1]
-      nc <- dims[2]
-      female_kin <- df[1:(nr/2), 1:nc]
-      male_kin <- df[ (1+nr/2) : nr, 1:nc]
-      female_kin$stage <- rep(seq(1, ns), na)
-      male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1)), each = ns)
-      male_kin$age <- rep(seq(0, (na-1)), each = ns)
-      female_kin$Sex <- "Female"
-      male_kin$Sex <- "Male"
-      both_kin <- rbind(female_kin, male_kin)
-      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
-        dplyr::transmute(age_focal = variable,
-                         age_kin = age,
-                         stage_kin = as.factor(stage),
-                         count = value,
-                         sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal))-1
-      df <- both_kin
-      df$year <- j
-      df$group <- kin_member
-      df_list[[length(df_list)+1]] <- df
-    }
-    df_list <- do.call("rbind", df_list)
-    df_year_list[[(1+length(df_year_list))]] <- df_list
-  }
-  df_year_list <- do.call("rbind", df_year_list)
-  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
-                                                 cohort_factor = as.factor(cohort))
-  if(specific_kin != FALSE){
-    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
-  }
-  return(df_year_list)
-}
-
-
-
-## Construct a matrix composed as a direct sum of a list of matrices
-block_diag_function <- function(mat_list){
-  s = length(mat_list)
-  u1 = mat_list[[1]]
-  dims <- dim(u1)
-  r = dims[1]
-  diagmat <- Matrix::Matrix(nrow = (r*s), ncol = (r*s), data = 0, sparse = TRUE)
-  for(i in 1:s){
-    diagmat = diagmat + kronecker(E_matrix(i,i,s,s), mat_list[[i]])
-  }
-  return(diagmat)
-}
-
-## Construct a matrix which transfers Focal across stages, while ensuring Focal survives with probability 1
-get_G <- function(U, na, ns){
-  sig <- Matrix::t(rep(1,na*ns)) %*% U
-  diag <- Matrix::diag(sig[1,])
-  G <- U %*% MASS::ginv(diag)
-  return(G)
-}
-
-#' Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
-#'
-#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
-#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix <- function(Uf, Um, Ff, Fm, alpha, na, ns){
-  n <- length(Uf[1,])
-  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  F_block[1:n, 1:n] <- (1-alpha)*Ff
-  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
-  A <- block_diag_function(list(Uf,Um)) + F_block
-  stable_dist_vec <- SD(A)
-  ### Joint distributions
-  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  ### Age distributions
-  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
-  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
-  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
-}
-
-#' Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
-#'
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix_TV <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
-  n <- length(Ff[1,])
-  ### Joint distributions
-  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  ### Age distributions
-  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
-  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
-  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
-}
-
-#' Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
-#'
-#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
-#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix_parity <- function(Uf, Um, Ff, Fm, alpha, na, ns){
-  n <- length(Uf[1,])
-  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  F_block[1:n, 1:n] <- (1-alpha)*Ff
-  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
-  A <- block_diag_function(list(Uf,Um)) + F_block
-  stable_dist_vec <- SD(A)
-  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  m_mat <- pi_f %*% Matrix::t(rep(1,na))
-  d_mat <- pi_m %*% Matrix::t(rep(1,na))
-  pi_F <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
-  pi_M <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
-  for(i in 1:na){
-    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
-    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
-  }
-  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
-  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
-  ### Joint distributions
-  pi_f <- out_mum %*% pi_F
-  pi_m <- out_dad %*% pi_M
-  return(list(c(pi_f,pi_m), pi_f, pi_m, pi_F, pi_M))
-}
-
-#' Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
-#'
-#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
-#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix_TV_parity <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
-  n <- length(Ff[1,])
-  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  m_mat <- pi_f %*% Matrix::t(rep(1,na))
-  d_mat <- pi_m %*% Matrix::t(rep(1,na))
-  pi_F  <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
-  pi_M <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
-  for(i in 1:na){
-    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
-    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
-  }
-  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
-  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
-  ### Joint distributions
-  pi_f <- out_mum %*% pi_F
-  pi_m <- out_dad %*% pi_M
-  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
-}
-
-
-######################################################### Some useful utility functions required
-
-
-###################################################### Eigen-decomposition of a matrix
-
-# Calculate the spectral radius of a matrix (growth rate in Demographics)
-lambda <- function(PM) {
-  lead_eig <- (abs(eigen(PM, only.values = TRUE)$values))
-  lead_eig <- lead_eig[which.max(lead_eig)]
-  return(lead_eig)
-}
-# Find the column-eigenvector corresponding to the spectral radius (Stable population structure in Demographics)
-SD <- function(PM) {
-  spectral_stuff <- eigen(PM)
-  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
-  # normalise...
-  vec_lambda <- spectral_stuff/sum(spectral_stuff)
-  return(vec_lambda)
-}
-# Find the row-eigenvector corresponding to the spectral radius (Stable reproductive values in Demographics)
-RD <- function(PM) {
-  spectral_stuff <- eigen(t(PM))
-  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
-  # normalise...
-  vec_lambda <- spectral_stuff/sum(spectral_stuff)
-  return(vec_lambda)
-}
-
-###################################################### Useful matrix operations
-
-## Constructing a unit vector with a 1 in the ith position
-e_vector <- function(i, n){
-  e <- rep(0, n)
-  e[i] <- 1
-  return(e)
-}
-## Creating a matrix of zeros with a 1 in the i,j-th entry
-E_matrix <- function(i,j,n,m){
-  E <- Matrix::Matrix(nrow = (n), ncol = (m), data = 0, sparse = TRUE)
-  E[i,j] <- 1
-  return(E)
-
-}
-## Creating the Vec-commutation matrix
-K_perm_mat <- function(n,m){
-  perm <- Matrix::Matrix(nrow = (n*m), ncol = (n*m), data = 0, sparse = TRUE)
-  for(i in 1:n){
-    for(j in 1:m){
-      perm = perm + kronecker( E_matrix(i,j,n,m) , Matrix::t(E_matrix(i,j,n,m)) )
-    }
-  }
-  return(perm)
-}
-
-
-
-
-
-
-
-

From 738cda7afb98c16fbe02562a51081cd134f3f4b3 Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Fri, 21 Feb 2025 15:41:21 +0000
Subject: [PATCH 64/89] fixing

---
 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index d3fb039..d2e4068 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -146,9 +146,9 @@ kin_out_1965_2005 <-
                                     summary_kin = TRUE,
                                     sex_Focal = "Female", ##  define Focal's sex at birth
                                     initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    output_years = c(1965, 1975, 1985, 1995, 2005), ## the sequence of years we run the function over
-                                    model_years <- seq(1965, 2005),
-                                    age_year_consitent = TRUE, ## we use 5-year age classes
+                                    output_years = c(1965, 1975, 1985, 1995, 2005), ## the sequence of years we want output
+                                    model_years <- seq(1965, 2005), ## the sequence of years we model
+                                    age_year_consitent = TRUE, ## 
                                     age_increment = NULL
 )
 

From 3025030905d0def3e43696ce8e3d54929dbadfaf Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 23 Apr 2025 20:40:24 -0300
Subject: [PATCH 65/89] home

---
 docs/404.html          | 121 +++++++++++++
 docs/LICENSE-text.html |  95 ++++++++++
 docs/LICENSE.html      |  99 +++++++++++
 docs/authors.html      | 126 ++++++++++++++
 docs/bootstrap-toc.css |  60 +++++++
 docs/bootstrap-toc.js  | 159 +++++++++++++++++
 docs/docsearch.css     | 148 ++++++++++++++++
 docs/docsearch.js      |  85 +++++++++
 docs/index.html        | 342 ++++++++++++++++++++++++++++++++++++
 docs/link.svg          |  12 ++
 docs/pkgdown.css       | 384 +++++++++++++++++++++++++++++++++++++++++
 docs/pkgdown.js        | 108 ++++++++++++
 docs/pkgdown.yml       |   8 +
 13 files changed, 1747 insertions(+)
 create mode 100644 docs/404.html
 create mode 100644 docs/LICENSE-text.html
 create mode 100644 docs/LICENSE.html
 create mode 100644 docs/authors.html
 create mode 100644 docs/bootstrap-toc.css
 create mode 100644 docs/bootstrap-toc.js
 create mode 100644 docs/docsearch.css
 create mode 100644 docs/docsearch.js
 create mode 100644 docs/index.html
 create mode 100644 docs/link.svg
 create mode 100644 docs/pkgdown.css
 create mode 100644 docs/pkgdown.js
 create mode 100644 docs/pkgdown.yml

diff --git a/docs/404.html b/docs/404.html
new file mode 100644
index 0000000..0f36184
--- /dev/null
+++ b/docs/404.html
@@ -0,0 +1,121 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Page not found (404) • DemoKin</title>
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
+<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
+<script src="pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
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+<body data-spy="scroll" data-target="#toc">
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+    <div class="container template-title-body">
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+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
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+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
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+    </li>
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+    </li>
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+<!--/.nav-collapse -->
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+<!--/.container -->
+</div>
+<!--/.navbar -->
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+      </header><div class="row">
+  <div class="contents col-md-9">
+    <div class="page-header">
+      <h1>Page not found (404)</h1>
+    </div>
+
+Content not found. Please use links in the navbar.
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diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
new file mode 100644
index 0000000..aa823db
--- /dev/null
+++ b/docs/LICENSE-text.html
@@ -0,0 +1,95 @@
+<!DOCTYPE html>
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+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
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+      <a href="articles/Reference_OneSex.html">Expected kin counts by type of relative in a one-sex framework</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex.html">Two-sex kinship model</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex_MultiState_TimeVariant.html">Expected kin counts by type of relative in a two-sex multi-state time-varying framework</a>
+    </li>
+  </ul></li>
+<li>
+  <a href="news/index.html">Changelog</a>
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+      <h1>License</h1>
+    </div>
+
+<pre>YEAR: 2021
+COPYRIGHT HOLDER: DemoKin authors
+</pre>
+
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
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+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
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new file mode 100644
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>MIT License • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="MIT License"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body data-spy="scroll" data-target="#toc">
+
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+    <div class="container template-title-body">
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+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
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+  <a href="reference/index.html">Reference</a>
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+    Articles
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+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu"><li>
+      <a href="articles/Reference_OneSex.html">Expected kin counts by type of relative in a one-sex framework</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex.html">Two-sex kinship model</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex_MultiState_TimeVariant.html">Expected kin counts by type of relative in a two-sex multi-state time-varying framework</a>
+    </li>
+  </ul></li>
+<li>
+  <a href="news/index.html">Changelog</a>
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+      </ul><ul class="nav navbar-nav navbar-right"><li>
+  <a href="https://github.com/IvanWilli/DemoKin/" class="external-link">
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+</div><!--/.navbar -->
+
+
+
+      </header><div class="row">
+  <div class="contents col-md-9">
+    <div class="page-header">
+      <h1>MIT License</h1>
+    </div>
+
+<div id="mit-license" class="section level1">
+
+<p>Copyright (c) 2021 DemoKin authors</p>
+<p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:</p>
+<p>The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.</p>
+<p>THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
+</div>
+
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
+    </nav></div>
+
+</div>
+
+
+
+      <footer><div class="copyright">
+  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+</div>
+
+<div class="pkgdown">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+</div>
+
+      </footer></div>
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+
diff --git a/docs/authors.html b/docs/authors.html
new file mode 100644
index 0000000..7b85e3a
--- /dev/null
+++ b/docs/authors.html
@@ -0,0 +1,126 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Authors and Citation • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+<![endif]--></head><body data-spy="scroll" data-target="#toc">
+
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+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav"><li>
+  <a href="reference/index.html">Reference</a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Articles
+
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu"><li>
+      <a href="articles/Reference_OneSex.html">Expected kin counts by type of relative in a one-sex framework</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex.html">Two-sex kinship model</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex_MultiState_TimeVariant.html">Expected kin counts by type of relative in a two-sex multi-state time-varying framework</a>
+    </li>
+  </ul></li>
+<li>
+  <a href="news/index.html">Changelog</a>
+</li>
+      </ul><ul class="nav navbar-nav navbar-right"><li>
+  <a href="https://github.com/IvanWilli/DemoKin/" class="external-link">
+    <span class="fab fa-github fa-lg"></span>
+
+  </a>
+</li>
+      </ul></div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+
+
+      </header><div class="row">
+  <div class="contents col-md-9">
+    <div class="section level2 authors-section">
+      <div class="page-header">
+        <h1>Authors and Citation</h1>
+      </div>
+
+
+      <ul class="list-unstyled"><li>
+          <p><strong>Iván Williams</strong>. Maintainer.
+          </p>
+        </li>
+        <li>
+          <p><strong>Diego Alburez-Gutierrez</strong>. Author.
+          </p>
+        </li>
+        <li>
+          <p><strong>Xi Song</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Caswell Hal</strong>. Contributor.
+          </p>
+        </li>
+      </ul></div>
+    <div class="section level2 citation-section">
+    <div>
+      <h1 id="citation">Citation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/IvanWilli/DemoKin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small>
+    </div>
+    </div>
+
+
+    <p>Alburez-Gutierrez D (2025).
+<em>DemoKin: Estimate Population Kin Distribution</em>.
+R package version 1.0.3, <a href="https://github.com/IvanWilli/DemoKin" class="external-link">https://github.com/IvanWilli/DemoKin</a>.
+</p>
+    <pre>@Manual{,
+  title = {DemoKin: Estimate Population Kin Distribution},
+  author = {Diego Alburez-Gutierrez},
+  year = {2025},
+  note = {R package version 1.0.3},
+  url = {https://github.com/IvanWilli/DemoKin},
+}</pre>
+
+  </div>
+
+</div>
+
+
+
+      <footer><div class="copyright">
+  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+</div>
+
+<div class="pkgdown">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+</div>
+
+      </footer></div>
+
+
+
+
+
+
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+
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new file mode 100644
index 0000000..5a85941
--- /dev/null
+++ b/docs/bootstrap-toc.css
@@ -0,0 +1,60 @@
+/*!
+ * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+
+/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */
+
+/* All levels of nav */
+nav[data-toggle='toc'] .nav > li > a {
+  display: block;
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+}
+nav[data-toggle='toc'] .nav > li > a:hover,
+nav[data-toggle='toc'] .nav > li > a:focus {
+  padding-left: 19px;
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+  background-color: transparent;
+  border-left: 1px solid #563d7c;
+}
+nav[data-toggle='toc'] .nav > .active > a,
+nav[data-toggle='toc'] .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav > .active:focus > a {
+  padding-left: 18px;
+  font-weight: bold;
+  color: #563d7c;
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+  border-left: 2px solid #563d7c;
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+/* Nav: second level (shown on .active) */
+nav[data-toggle='toc'] .nav .nav {
+  display: none; /* Hide by default, but at >768px, show it */
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+nav[data-toggle='toc'] .nav .nav > li > a {
+  padding-top: 1px;
+  padding-bottom: 1px;
+  padding-left: 30px;
+  font-size: 12px;
+  font-weight: normal;
+}
+nav[data-toggle='toc'] .nav .nav > li > a:hover,
+nav[data-toggle='toc'] .nav .nav > li > a:focus {
+  padding-left: 29px;
+}
+nav[data-toggle='toc'] .nav .nav > .active > a,
+nav[data-toggle='toc'] .nav .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav .nav > .active:focus > a {
+  padding-left: 28px;
+  font-weight: 500;
+}
+
+/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */
+nav[data-toggle='toc'] .nav > .active > ul {
+  display: block;
+}
diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js
new file mode 100644
index 0000000..1cdd573
--- /dev/null
+++ b/docs/bootstrap-toc.js
@@ -0,0 +1,159 @@
+/*!
+ * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+(function() {
+  'use strict';
+
+  window.Toc = {
+    helpers: {
+      // return all matching elements in the set, or their descendants
+      findOrFilter: function($el, selector) {
+        // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/
+        // http://stackoverflow.com/a/12731439/358804
+        var $descendants = $el.find(selector);
+        return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])');
+      },
+
+      generateUniqueIdBase: function(el) {
+        var text = $(el).text();
+        var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-');
+        return anchor || el.tagName.toLowerCase();
+      },
+
+      generateUniqueId: function(el) {
+        var anchorBase = this.generateUniqueIdBase(el);
+        for (var i = 0; ; i++) {
+          var anchor = anchorBase;
+          if (i > 0) {
+            // add suffix
+            anchor += '-' + i;
+          }
+          // check if ID already exists
+          if (!document.getElementById(anchor)) {
+            return anchor;
+          }
+        }
+      },
+
+      generateAnchor: function(el) {
+        if (el.id) {
+          return el.id;
+        } else {
+          var anchor = this.generateUniqueId(el);
+          el.id = anchor;
+          return anchor;
+        }
+      },
+
+      createNavList: function() {
+        return $('<ul class="nav"></ul>');
+      },
+
+      createChildNavList: function($parent) {
+        var $childList = this.createNavList();
+        $parent.append($childList);
+        return $childList;
+      },
+
+      generateNavEl: function(anchor, text) {
+        var $a = $('<a></a>');
+        $a.attr('href', '#' + anchor);
+        $a.text(text);
+        var $li = $('<li></li>');
+        $li.append($a);
+        return $li;
+      },
+
+      generateNavItem: function(headingEl) {
+        var anchor = this.generateAnchor(headingEl);
+        var $heading = $(headingEl);
+        var text = $heading.data('toc-text') || $heading.text();
+        return this.generateNavEl(anchor, text);
+      },
+
+      // Find the first heading level (`<h1>`, then `<h2>`, etc.) that has more than one element. Defaults to 1 (for `<h1>`).
+      getTopLevel: function($scope) {
+        for (var i = 1; i <= 6; i++) {
+          var $headings = this.findOrFilter($scope, 'h' + i);
+          if ($headings.length > 1) {
+            return i;
+          }
+        }
+
+        return 1;
+      },
+
+      // returns the elements for the top level, and the next below it
+      getHeadings: function($scope, topLevel) {
+        var topSelector = 'h' + topLevel;
+
+        var secondaryLevel = topLevel + 1;
+        var secondarySelector = 'h' + secondaryLevel;
+
+        return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
+      },
+
+      getNavLevel: function(el) {
+        return parseInt(el.tagName.charAt(1), 10);
+      },
+
+      populateNav: function($topContext, topLevel, $headings) {
+        var $context = $topContext;
+        var $prevNav;
+
+        var helpers = this;
+        $headings.each(function(i, el) {
+          var $newNav = helpers.generateNavItem(el);
+          var navLevel = helpers.getNavLevel(el);
+
+          // determine the proper $context
+          if (navLevel === topLevel) {
+            // use top level
+            $context = $topContext;
+          } else if ($prevNav && $context === $topContext) {
+            // create a new level of the tree and switch to it
+            $context = helpers.createChildNavList($prevNav);
+          } // else use the current $context
+
+          $context.append($newNav);
+
+          $prevNav = $newNav;
+        });
+      },
+
+      parseOps: function(arg) {
+        var opts;
+        if (arg.jquery) {
+          opts = {
+            $nav: arg
+          };
+        } else {
+          opts = arg;
+        }
+        opts.$scope = opts.$scope || $(document.body);
+        return opts;
+      }
+    },
+
+    // accepts a jQuery object, or an options object
+    init: function(opts) {
+      opts = this.helpers.parseOps(opts);
+
+      // ensure that the data attribute is in place for styling
+      opts.$nav.attr('data-toggle', 'toc');
+
+      var $topContext = this.helpers.createChildNavList(opts.$nav);
+      var topLevel = this.helpers.getTopLevel(opts.$scope);
+      var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
+      this.helpers.populateNav($topContext, topLevel, $headings);
+    }
+  };
+
+  $(function() {
+    $('nav[data-toggle="toc"]').each(function(i, el) {
+      var $nav = $(el);
+      Toc.init($nav);
+    });
+  });
+})();
diff --git a/docs/docsearch.css b/docs/docsearch.css
new file mode 100644
index 0000000..e5f1fe1
--- /dev/null
+++ b/docs/docsearch.css
@@ -0,0 +1,148 @@
+/* Docsearch -------------------------------------------------------------- */
+/*
+  Source: https://github.com/algolia/docsearch/
+  License: MIT
+*/
+
+.algolia-autocomplete {
+  display: block;
+  -webkit-box-flex: 1;
+  -ms-flex: 1;
+  flex: 1
+}
+
+.algolia-autocomplete .ds-dropdown-menu {
+  width: 100%;
+  min-width: none;
+  max-width: none;
+  padding: .75rem 0;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, .1);
+  box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
+}
+
+@media (min-width:768px) {
+  .algolia-autocomplete .ds-dropdown-menu {
+      width: 175%
+  }
+}
+
+.algolia-autocomplete .ds-dropdown-menu::before {
+  display: none
+}
+
+.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
+  padding: 0;
+  background-color: rgb(255,255,255);
+  border: 0;
+  max-height: 80vh;
+}
+
+.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
+  margin-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion {
+  padding: 0;
+  overflow: visible
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
+  padding: .125rem 1rem;
+  margin-top: 0;
+  font-size: 1.3em;
+  font-weight: 500;
+  color: #00008B;
+  border-bottom: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
+    float: none;
+    padding-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
+  float: none;
+  width: auto;
+  padding: 0;
+  text-align: left
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content {
+  float: none;
+  width: auto;
+  padding: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content::before {
+  display: none
+}
+
+.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
+  padding-top: .75rem;
+  margin-top: .75rem;
+  border-top: 1px solid rgba(0, 0, 0, .1)
+}
+
+.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column {
+  display: block;
+  padding: .1rem 1rem;
+  margin-bottom: 0.1;
+  font-size: 1.0em;
+  font-weight: 400
+  /* display: none */
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--title {
+  display: block;
+  padding: .25rem 1rem;
+  margin-bottom: 0;
+  font-size: 0.9em;
+  font-weight: 400
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text {
+  padding: 0 1rem .5rem;
+  margin-top: -.25rem;
+  font-size: 0.8em;
+  font-weight: 400;
+  line-height: 1.25
+}
+
+.algolia-autocomplete .algolia-docsearch-footer {
+  width: 110px;
+  height: 20px;
+  z-index: 3;
+  margin-top: 10.66667px;
+  float: right;
+  font-size: 0;
+  line-height: 0;
+}
+
+.algolia-autocomplete .algolia-docsearch-footer--logo {
+  background-image: url("data:image/svg+xml;utf8,<svg viewBox='0 0 130 18' xmlns='http://www.w3.org/2000/svg'><defs><linearGradient x1='-36.868%' y1='134.936%' x2='129.432%' y2='-27.7%' id='a'><stop stop-color='%2300AEFF' offset='0%'/><stop stop-color='%233369E7' offset='100%'/></linearGradient></defs><g fill='none' fill-rule='evenodd'><path d='M59.399.022h13.299a2.372 2.372 0 0 1 2.377 2.364V15.62a2.372 2.372 0 0 1-2.377 2.364H59.399a2.372 2.372 0 0 1-2.377-2.364V2.381A2.368 2.368 0 0 1 59.399.022z' fill='url(%23a)'/><path d='M66.257 4.56c-2.815 0-5.1 2.272-5.1 5.078 0 2.806 2.284 5.072 5.1 5.072 2.815 0 5.1-2.272 5.1-5.078 0-2.806-2.279-5.072-5.1-5.072zm0 8.652c-1.983 0-3.593-1.602-3.593-3.574 0-1.972 1.61-3.574 3.593-3.574 1.983 0 3.593 1.602 3.593 3.574a3.582 3.582 0 0 1-3.593 3.574zm0-6.418v2.664c0 .076.082.131.153.093l2.377-1.226c.055-.027.071-.093.044-.147a2.96 2.96 0 0 0-2.465-1.487c-.055 0-.11.044-.11.104l.001-.001zm-3.33-1.956l-.312-.311a.783.783 0 0 0-1.106 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+  background-repeat: no-repeat;
+  background-position: 50%;
+  background-size: 100%;
+  overflow: hidden;
+  text-indent: -9000px;
+  width: 100%;
+  height: 100%;
+  display: block;
+  transform: translate(-8px);
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--highlight {
+  color: #FF8C00;
+  background: rgba(232, 189, 54, 0.1)
+}
+
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight {
+  box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
+}
+
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+<script src="pkgdown.js"></script><meta property="og:title" content="Estimate Population Kin Distribution">
+<meta property="og:description" content="Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.">
+<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+<li>
+  <a href="reference/index.html">Reference</a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Articles
+
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+<li>
+      <a href="articles/Reference_OneSex.html">Expected kin counts by type of relative in a one-sex framework</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex.html">Two-sex kinship model</a>
+    </li>
+    <li>
+      <a href="articles/Reference_TwoSex_MultiState_TimeVariant.html">Expected kin counts by type of relative in a two-sex multi-state time-varying framework</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="news/index.html">Changelog</a>
+</li>
+      </ul>
+<ul class="nav navbar-nav navbar-right">
+<li>
+  <a href="https://github.com/IvanWilli/DemoKin/" class="external-link">
+    <span class="fab fa-github fa-lg"></span>
+
+  </a>
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+
+
+
+      </header><div class="row">
+  <div class="contents col-md-9">
+<div class="section level1">
+<div class="page-header"><h1 id="demokin">DemoKin<a class="anchor" aria-label="anchor" href="#demokin"></a>
+</h1></div>
+<div class="columns">
+<div class="column" style="width:60%;">
+<p><code>DemoKin</code> uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell [-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2022], and Caswell and Song [-@caswell_formal_2021]. It draws on previous theoretical development by Goodman, Keyfitz and Pullum [-@goodman_family_1974].</p>
+</div>
+<div class="column" style="width:40%;">
+<p><img src="reference/figures/DemoKin-Logo.png" align="right" width="200"></p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
+</h2>
+<p>Download the stable version <a href="https://cran.r-project.org/web/packages/DemoKin/" class="external-link">from CRAN</a>:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"DemoKin"</span><span class="op">)</span></span></code></pre></div>
+<p>Or you can install the development version from GitHub:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># install.packages("devtools")</span></span>
+<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"IvanWilli/DemoKin"</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a>
+</h2>
+<p>Consider an average Swedish woman called ‘Focal.’ For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life; i.e., the ‘time-invariant’ assumption in Caswell [-@caswell_formal_2019].</p>
+<p>We then ask:</p>
+<blockquote>
+<p>What is the expected number of relatives of Focal over her life course?</p>
+</blockquote>
+<p>Let’s explore this using the Swedish data already included with <code>DemoKin</code>.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/IvanWilli/DemoKin" class="external-link">DemoKin</a></span><span class="op">)</span></span>
+<span><span class="va">swe_surv_2015</span> <span class="op">&lt;-</span> <span class="va">swe_px</span><span class="op">[</span>,<span class="st">"2015"</span><span class="op">]</span></span>
+<span><span class="va">swe_asfr_2015</span> <span class="op">&lt;-</span> <span class="va">swe_asfr</span><span class="op">[</span>,<span class="st">"2015"</span><span class="op">]</span></span>
+<span><span class="va">swe_2015</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/kin.html">kin</a></span><span class="op">(</span>p <span class="op">=</span> <span class="va">swe_surv_2015</span>, f <span class="op">=</span> <span class="va">swe_asfr_2015</span>, time_invariant <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p><em>p</em> is the survival probability by age from a life table and <em>f</em> are the age specific fertility ratios by age (see <code><a href="reference/kin.html">?kin</a></code> for details).</p>
+<p>Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or ‘Keyfitz’ kinship diagram with the function <code>plot_diagram</code>:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># We need to reformat the data a little bit</span></span>
+<span><span class="va">kin_total</span> <span class="op">&lt;-</span> <span class="va">swe_2015</span><span class="op">$</span><span class="va">kin_summary</span></span>
+<span><span class="co"># Keep only data for Focal's age 35</span></span>
+<span><span class="va">kin_total</span> <span class="op">&lt;-</span> <span class="va">kin_total</span><span class="op">[</span><span class="va">kin_total</span><span class="op">$</span><span class="va">age_focal</span> <span class="op">==</span> <span class="fl">35</span> , <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"kin"</span>, <span class="st">"count_living"</span><span class="op">)</span><span class="op">]</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">kin_total</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"kin"</span>, <span class="st">"count"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="reference/plot_diagram.html">plot_diagram</a></span><span class="op">(</span><span class="va">kin_total</span>, rounding <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
+<p><img src="reference/figures/README-unnamed-chunk-5-1.png" width="100%"></p>
+<p>Relatives are identified by a unique code:</p>
+<table class="table">
+<colgroup>
+<col width="8%">
+<col width="27%">
+<col width="29%">
+<col width="33%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">DemoKin</th>
+<th align="left">Labels_female</th>
+<th align="left">Labels_male</th>
+<th align="left">Labels_2sex</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">coa</td>
+<td align="left">Cousins from older aunts</td>
+<td align="left">Cousins from older uncles</td>
+<td align="left">Cousins from older aunts/uncles</td>
+</tr>
+<tr class="even">
+<td align="left">cya</td>
+<td align="left">Cousins from younger aunts</td>
+<td align="left">Cousins from younger uncles</td>
+<td align="left">Cousins from younger aunts/uncles</td>
+</tr>
+<tr class="odd">
+<td align="left">c</td>
+<td align="left">Cousins</td>
+<td align="left">Cousins</td>
+<td align="left">Cousins</td>
+</tr>
+<tr class="even">
+<td align="left">d</td>
+<td align="left">Daughters</td>
+<td align="left">Sons</td>
+<td align="left">Children</td>
+</tr>
+<tr class="odd">
+<td align="left">gd</td>
+<td align="left">Grand-daughters</td>
+<td align="left">Grand-sons</td>
+<td align="left">Grand-childrens</td>
+</tr>
+<tr class="even">
+<td align="left">ggd</td>
+<td align="left">Great-grand-daughters</td>
+<td align="left">Great-grand-sons</td>
+<td align="left">Great-grand-childrens</td>
+</tr>
+<tr class="odd">
+<td align="left">ggm</td>
+<td align="left">Great-grandmothers</td>
+<td align="left">Great-grandfathers</td>
+<td align="left">Great-grandfparents</td>
+</tr>
+<tr class="even">
+<td align="left">gm</td>
+<td align="left">Grandmothers</td>
+<td align="left">Grandfathers</td>
+<td align="left">Grandparents</td>
+</tr>
+<tr class="odd">
+<td align="left">m</td>
+<td align="left">Mother</td>
+<td align="left">Father</td>
+<td align="left">Parents</td>
+</tr>
+<tr class="even">
+<td align="left">nos</td>
+<td align="left">Nieces from older sisters</td>
+<td align="left">Nephews from older brothers</td>
+<td align="left">Niblings from older siblings</td>
+</tr>
+<tr class="odd">
+<td align="left">nys</td>
+<td align="left">Nieces from younger sisters</td>
+<td align="left">Nephews from younger brothers</td>
+<td align="left">Niblings from younger siblings</td>
+</tr>
+<tr class="even">
+<td align="left">n</td>
+<td align="left">Nieces</td>
+<td align="left">Nephews</td>
+<td align="left">Niblings</td>
+</tr>
+<tr class="odd">
+<td align="left">oa</td>
+<td align="left">Aunts older than mother</td>
+<td align="left">Uncles older than fathers</td>
+<td align="left">Aunts/Uncles older than parents</td>
+</tr>
+<tr class="even">
+<td align="left">ya</td>
+<td align="left">Aunts younger than mother</td>
+<td align="left">Uncles younger than father</td>
+<td align="left">Aunts/Uncles younger than parents</td>
+</tr>
+<tr class="odd">
+<td align="left">a</td>
+<td align="left">Aunts</td>
+<td align="left">Uncles</td>
+<td align="left">Aunts/Uncles</td>
+</tr>
+<tr class="even">
+<td align="left">os</td>
+<td align="left">Older sisters</td>
+<td align="left">Older brothers</td>
+<td align="left">Older siblings</td>
+</tr>
+<tr class="odd">
+<td align="left">ys</td>
+<td align="left">Younger sisters</td>
+<td align="left">Younger brothers</td>
+<td align="left">Younger siblings</td>
+</tr>
+<tr class="even">
+<td align="left">s</td>
+<td align="left">Sisters</td>
+<td align="left">Brothers</td>
+<td align="left">Siblings</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section level2">
+<h2 id="vignette">Vignette<a class="anchor" aria-label="anchor" href="#vignette"></a>
+</h2>
+<p>For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html" class="external-link">Reference_OneSex</a> vignette; also accessible from DemoKin: <code><a href="articles/Reference_OneSex.html">vignette("Reference_OneSex", package = "DemoKin")</a></code>. For two-sex models, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html" class="external-link">Reference_TwoSex</a> vignette; also accessible from DemoKin: <code><a href="articles/Reference_TwoSex.html">vignette("Reference_TwoSex", package = "DemoKin")</a></code>. If the vignette does not load, you may need to install the package as <code>devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)</code>.</p>
+</div>
+<div class="section level2">
+<h2 id="citation">Citation<a class="anchor" aria-label="anchor" href="#citation"></a>
+</h2>
+<p>Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: <a href="https://github.com/IvanWilli/DemoKin" class="external-link uri">https://github.com/IvanWilli/DemoKin</a>.</p>
+</div>
+<div class="section level2">
+<h2 id="acknowledgments">Acknowledgments<a class="anchor" aria-label="anchor" href="#acknowledgments"></a>
+</h2>
+<p>We thank Silvia Leek from the Max Planck Institute for Demographic Research for designing the DemoKin logo. The logo includes elements that have been taken or adapted <a href="https://commons.wikimedia.org/wiki/File:Escudo_de_la_Orden_de_San_Jer%C3%B3nimo.svg" class="external-link">from this file</a>, originally by Ansunando, <a href="https://creativecommons.org/licenses/by-sa/4.0" class="external-link">CC BY-SA 4.0</a> via Wikimedia Commons. Sha Jiang provided useful comments for improving the package.</p>
+</div>
+<div class="section level2">
+<h2 id="get-involved">Get involved!<a class="anchor" aria-label="anchor" href="#get-involved"></a>
+</h2>
+<p><code>DemoKin</code> is under constant development. If you’re interested in contributing, please get in touch, create an issue, or submit a pull request. We look forward to hearing from you!</p>
+<!-- ## References -->
+</div>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <div class="links">
+<h2 data-toc-skip>Links</h2>
+<ul class="list-unstyled">
+<li><a href="https://cloud.r-project.org/package=DemoKin" class="external-link">View on CRAN</a></li>
+<li><a href="https://github.com/IvanWilli/DemoKin/" class="external-link">Browse source code</a></li>
+<li><a href="https://github.com/IvanWilli/DemoKin/issues" class="external-link">Report a bug</a></li>
+</ul>
+</div>
+
+<div class="license">
+<h2 data-toc-skip>License</h2>
+<ul class="list-unstyled">
+<li><a href="LICENSE.html">Full license</a></li>
+<li><small><a href="https://opensource.org/licenses/mit-license.php" class="external-link">MIT</a> + file <a href="LICENSE-text.html">LICENSE</a></small></li>
+</ul>
+</div>
+
+
+<div class="citation">
+<h2 data-toc-skip>Citation</h2>
+<ul class="list-unstyled">
+<li><a href="authors.html#citation">Citing DemoKin</a></li>
+</ul>
+</div>
+
+<div class="developers">
+<h2 data-toc-skip>Developers</h2>
+<ul class="list-unstyled">
+<li>Iván Williams <br><small class="roles"> Maintainer </small>   </li>
+<li>Diego Alburez-Gutierrez <br><small class="roles"> Author </small>   </li>
+<li><a href="authors.html">More about authors...</a></li>
+</ul>
+</div>
+
+
+
+  </div>
+</div>
+
+
+      <footer><div class="copyright">
+  <p></p>
+<p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+</div>
+
+<div class="pkgdown">
+  <p></p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+</div>
+
+      </footer>
+</div>
+
+
+
+
+
+
+  </body>
+</html>
diff --git a/docs/link.svg b/docs/link.svg
new file mode 100644
index 0000000..88ad827
--- /dev/null
+++ b/docs/link.svg
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 19.2.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 20 20" style="enable-background:new 0 0 20 20;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#75AADB;}
+</style>
+<path class="st0" d="M4,11.3h1.3v1.3H4c-2,0-4-2.3-4-4.7s2.1-4.7,4-4.7h5.3c1.9,0,4,2.3,4,4.7c0,1.9-1.2,3.6-2.7,4.3v-1.5
+	C11.4,10.2,12,9.1,12,8c0-1.7-1.4-3.3-2.7-3.3H4C2.7,4.7,1.3,6.3,1.3,8S2.7,11.3,4,11.3z M16,7.3h-1.3v1.3H16c1.3,0,2.7,1.6,2.7,3.3
+	s-1.4,3.3-2.7,3.3h-5.3C9.4,15.3,8,13.7,8,12c0-1.1,0.6-2.2,1.3-2.8V7.7C7.9,8.4,6.7,10.1,6.7,12c0,2.4,2.1,4.7,4,4.7H16
+	c1.9,0,4-2.3,4-4.7S18,7.3,16,7.3z"/>
+</svg>
diff --git a/docs/pkgdown.css b/docs/pkgdown.css
new file mode 100644
index 0000000..80ea5b8
--- /dev/null
+++ b/docs/pkgdown.css
@@ -0,0 +1,384 @@
+/* Sticky footer */
+
+/**
+ * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
+ * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
+ *
+ * .Site -> body > .container
+ * .Site-content -> body > .container .row
+ * .footer -> footer
+ *
+ * Key idea seems to be to ensure that .container and __all its parents__
+ * have height set to 100%
+ *
+ */
+
+html, body {
+  height: 100%;
+}
+
+body {
+  position: relative;
+}
+
+body > .container {
+  display: flex;
+  height: 100%;
+  flex-direction: column;
+}
+
+body > .container .row {
+  flex: 1 0 auto;
+}
+
+footer {
+  margin-top: 45px;
+  padding: 35px 0 36px;
+  border-top: 1px solid #e5e5e5;
+  color: #666;
+  display: flex;
+  flex-shrink: 0;
+}
+footer p {
+  margin-bottom: 0;
+}
+footer div {
+  flex: 1;
+}
+footer .pkgdown {
+  text-align: right;
+}
+footer p {
+  margin-bottom: 0;
+}
+
+img.icon {
+  float: right;
+}
+
+/* Ensure in-page images don't run outside their container */
+.contents img {
+  max-width: 100%;
+  height: auto;
+}
+
+/* Fix bug in bootstrap (only seen in firefox) */
+summary {
+  display: list-item;
+}
+
+/* Typographic tweaking ---------------------------------*/
+
+.contents .page-header {
+  margin-top: calc(-60px + 1em);
+}
+
+dd {
+  margin-left: 3em;
+}
+
+/* Section anchors ---------------------------------*/
+
+a.anchor {
+  display: none;
+  margin-left: 5px;
+  width: 20px;
+  height: 20px;
+
+  background-image: url(./link.svg);
+  background-repeat: no-repeat;
+  background-size: 20px 20px;
+  background-position: center center;
+}
+
+h1:hover .anchor,
+h2:hover .anchor,
+h3:hover .anchor,
+h4:hover .anchor,
+h5:hover .anchor,
+h6:hover .anchor {
+  display: inline-block;
+}
+
+/* Fixes for fixed navbar --------------------------*/
+
+.contents h1, .contents h2, .contents h3, .contents h4 {
+  padding-top: 60px;
+  margin-top: -40px;
+}
+
+/* Navbar submenu --------------------------*/
+
+.dropdown-submenu {
+  position: relative;
+}
+
+.dropdown-submenu>.dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+  border-radius: 0 6px 6px 6px;
+}
+
+.dropdown-submenu:hover>.dropdown-menu {
+  display: block;
+}
+
+.dropdown-submenu>a:after {
+  display: block;
+  content: " ";
+  float: right;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #cccccc;
+  margin-top: 5px;
+  margin-right: -10px;
+}
+
+.dropdown-submenu:hover>a:after {
+  border-left-color: #ffffff;
+}
+
+.dropdown-submenu.pull-left {
+  float: none;
+}
+
+.dropdown-submenu.pull-left>.dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+  border-radius: 6px 0 6px 6px;
+}
+
+/* Sidebar --------------------------*/
+
+#pkgdown-sidebar {
+  margin-top: 30px;
+  position: -webkit-sticky;
+  position: sticky;
+  top: 70px;
+}
+
+#pkgdown-sidebar h2 {
+  font-size: 1.5em;
+  margin-top: 1em;
+}
+
+#pkgdown-sidebar h2:first-child {
+  margin-top: 0;
+}
+
+#pkgdown-sidebar .list-unstyled li {
+  margin-bottom: 0.5em;
+}
+
+/* bootstrap-toc tweaks ------------------------------------------------------*/
+
+/* All levels of nav */
+
+nav[data-toggle='toc'] .nav > li > a {
+  padding: 4px 20px 4px 6px;
+  font-size: 1.5rem;
+  font-weight: 400;
+  color: inherit;
+}
+
+nav[data-toggle='toc'] .nav > li > a:hover,
+nav[data-toggle='toc'] .nav > li > a:focus {
+  padding-left: 5px;
+  color: inherit;
+  border-left: 1px solid #878787;
+}
+
+nav[data-toggle='toc'] .nav > .active > a,
+nav[data-toggle='toc'] .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav > .active:focus > a {
+  padding-left: 5px;
+  font-size: 1.5rem;
+  font-weight: 400;
+  color: inherit;
+  border-left: 2px solid #878787;
+}
+
+/* Nav: second level (shown on .active) */
+
+nav[data-toggle='toc'] .nav .nav {
+  display: none; /* Hide by default, but at >768px, show it */
+  padding-bottom: 10px;
+}
+
+nav[data-toggle='toc'] .nav .nav > li > a {
+  padding-left: 16px;
+  font-size: 1.35rem;
+}
+
+nav[data-toggle='toc'] .nav .nav > li > a:hover,
+nav[data-toggle='toc'] .nav .nav > li > a:focus {
+  padding-left: 15px;
+}
+
+nav[data-toggle='toc'] .nav .nav > .active > a,
+nav[data-toggle='toc'] .nav .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav .nav > .active:focus > a {
+  padding-left: 15px;
+  font-weight: 500;
+  font-size: 1.35rem;
+}
+
+/* orcid ------------------------------------------------------------------- */
+
+.orcid {
+  font-size: 16px;
+  color: #A6CE39;
+  /* margins are required by official ORCID trademark and display guidelines */
+  margin-left:4px;
+  margin-right:4px;
+  vertical-align: middle;
+}
+
+/* Reference index & topics ----------------------------------------------- */
+
+.ref-index th {font-weight: normal;}
+
+.ref-index td {vertical-align: top; min-width: 100px}
+.ref-index .icon {width: 40px;}
+.ref-index .alias {width: 40%;}
+.ref-index-icons .alias {width: calc(40% - 40px);}
+.ref-index .title {width: 60%;}
+
+.ref-arguments th {text-align: right; padding-right: 10px;}
+.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
+.ref-arguments .name {width: 20%;}
+.ref-arguments .desc {width: 80%;}
+
+/* Nice scrolling for wide elements --------------------------------------- */
+
+table {
+  display: block;
+  overflow: auto;
+}
+
+/* Syntax highlighting ---------------------------------------------------- */
+
+pre, code, pre code {
+  background-color: #f8f8f8;
+  color: #333;
+}
+pre, pre code {
+  white-space: pre-wrap;
+  word-break: break-all;
+  overflow-wrap: break-word;
+}
+
+pre {
+  border: 1px solid #eee;
+}
+
+pre .img, pre .r-plt {
+  margin: 5px 0;
+}
+
+pre .img img, pre .r-plt img {
+  background-color: #fff;
+}
+
+code a, pre a {
+  color: #375f84;
+}
+
+a.sourceLine:hover {
+  text-decoration: none;
+}
+
+.fl      {color: #1514b5;}
+.fu      {color: #000000;} /* function */
+.ch,.st  {color: #036a07;} /* string */
+.kw      {color: #264D66;} /* keyword */
+.co      {color: #888888;} /* comment */
+
+.error   {font-weight: bolder;}
+.warning {font-weight: bolder;}
+
+/* Clipboard --------------------------*/
+
+.hasCopyButton {
+  position: relative;
+}
+
+.btn-copy-ex {
+  position: absolute;
+  right: 0;
+  top: 0;
+  visibility: hidden;
+}
+
+.hasCopyButton:hover button.btn-copy-ex {
+  visibility: visible;
+}
+
+/* headroom.js ------------------------ */
+
+.headroom {
+  will-change: transform;
+  transition: transform 200ms linear;
+}
+.headroom--pinned {
+  transform: translateY(0%);
+}
+.headroom--unpinned {
+  transform: translateY(-100%);
+}
+
+/* mark.js ----------------------------*/
+
+mark {
+  background-color: rgba(255, 255, 51, 0.5);
+  border-bottom: 2px solid rgba(255, 153, 51, 0.3);
+  padding: 1px;
+}
+
+/* vertical spacing after htmlwidgets */
+.html-widget {
+  margin-bottom: 10px;
+}
+
+/* fontawesome ------------------------ */
+
+.fab {
+    font-family: "Font Awesome 5 Brands" !important;
+}
+
+/* don't display links in code chunks when printing */
+/* source: https://stackoverflow.com/a/10781533 */
+@media print {
+  code a:link:after, code a:visited:after {
+    content: "";
+  }
+}
+
+/* Section anchors ---------------------------------
+   Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
+*/
+
+div.csl-bib-body { }
+div.csl-entry {
+  clear: both;
+}
+.hanging-indent div.csl-entry {
+  margin-left:2em;
+  text-indent:-2em;
+}
+div.csl-left-margin {
+  min-width:2em;
+  float:left;
+}
+div.csl-right-inline {
+  margin-left:2em;
+  padding-left:1em;
+}
+div.csl-indent {
+  margin-left: 2em;
+}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
new file mode 100644
index 0000000..6f0eee4
--- /dev/null
+++ b/docs/pkgdown.js
@@ -0,0 +1,108 @@
+/* http://gregfranko.com/blog/jquery-best-practices/ */
+(function($) {
+  $(function() {
+
+    $('.navbar-fixed-top').headroom();
+
+    $('body').css('padding-top', $('.navbar').height() + 10);
+    $(window).resize(function(){
+      $('body').css('padding-top', $('.navbar').height() + 10);
+    });
+
+    $('[data-toggle="tooltip"]').tooltip();
+
+    var cur_path = paths(location.pathname);
+    var links = $("#navbar ul li a");
+    var max_length = -1;
+    var pos = -1;
+    for (var i = 0; i < links.length; i++) {
+      if (links[i].getAttribute("href") === "#")
+        continue;
+      // Ignore external links
+      if (links[i].host !== location.host)
+        continue;
+
+      var nav_path = paths(links[i].pathname);
+
+      var length = prefix_length(nav_path, cur_path);
+      if (length > max_length) {
+        max_length = length;
+        pos = i;
+      }
+    }
+
+    // Add class to parent <li>, and enclosing <li> if in dropdown
+    if (pos >= 0) {
+      var menu_anchor = $(links[pos]);
+      menu_anchor.parent().addClass("active");
+      menu_anchor.closest("li.dropdown").addClass("active");
+    }
+  });
+
+  function paths(pathname) {
+    var pieces = pathname.split("/");
+    pieces.shift(); // always starts with /
+
+    var end = pieces[pieces.length - 1];
+    if (end === "index.html" || end === "")
+      pieces.pop();
+    return(pieces);
+  }
+
+  // Returns -1 if not found
+  function prefix_length(needle, haystack) {
+    if (needle.length > haystack.length)
+      return(-1);
+
+    // Special case for length-0 haystack, since for loop won't run
+    if (haystack.length === 0) {
+      return(needle.length === 0 ? 0 : -1);
+    }
+
+    for (var i = 0; i < haystack.length; i++) {
+      if (needle[i] != haystack[i])
+        return(i);
+    }
+
+    return(haystack.length);
+  }
+
+  /* Clipboard --------------------------*/
+
+  function changeTooltipMessage(element, msg) {
+    var tooltipOriginalTitle=element.getAttribute('data-original-title');
+    element.setAttribute('data-original-title', msg);
+    $(element).tooltip('show');
+    element.setAttribute('data-original-title', tooltipOriginalTitle);
+  }
+
+  if(ClipboardJS.isSupported()) {
+    $(document).ready(function() {
+      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
+
+      $("div.sourceCode").addClass("hasCopyButton");
+
+      // Insert copy buttons:
+      $(copyButton).prependTo(".hasCopyButton");
+
+      // Initialize tooltips:
+      $('.btn-copy-ex').tooltip({container: 'body'});
+
+      // Initialize clipboard:
+      var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
+        text: function(trigger) {
+          return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
+        }
+      });
+
+      clipboardBtnCopies.on('success', function(e) {
+        changeTooltipMessage(e.trigger, 'Copied!');
+        e.clearSelection();
+      });
+
+      clipboardBtnCopies.on('error', function() {
+        changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
+      });
+    });
+  }
+})(window.jQuery || window.$)
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
new file mode 100644
index 0000000..81ed8a8
--- /dev/null
+++ b/docs/pkgdown.yml
@@ -0,0 +1,8 @@
+pandoc: 3.1.1
+pkgdown: 2.1.1.9000
+pkgdown_sha: 62d5c5a0fc12899aae97f6ce14018d2784bf308b
+articles:
+  Reference_OneSex: Reference_OneSex.html
+  Reference_TwoSex: Reference_TwoSex.html
+  Reference_TwoSex_MultiState_TimeVariant: Reference_TwoSex_MultiState_TimeVariant.html
+last_built: 2025-04-23T23:39Z

From 52ea6bace908e7c4066efc1c0dcef0d22d86b164 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 23 Apr 2025 20:42:40 -0300
Subject: [PATCH 66/89] home git

---
 .Rbuildignore                  |  4 +++
 .github/.gitignore             |  1 +
 .github/workflows/pkgdown.yaml | 49 ++++++++++++++++++++++++++++++++++
 .gitignore                     |  1 +
 DESCRIPTION                    |  3 ++-
 _pkgdown.yml                   |  5 ++++
 6 files changed, 62 insertions(+), 1 deletion(-)
 create mode 100644 .github/.gitignore
 create mode 100644 .github/workflows/pkgdown.yaml
 create mode 100644 _pkgdown.yml

diff --git a/.Rbuildignore b/.Rbuildignore
index 7b0fa46..07cff67 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -4,3 +4,7 @@
 ^LICENSE\.md$
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
+^\.github$
diff --git a/.github/.gitignore b/.github/.gitignore
new file mode 100644
index 0000000..2d19fc7
--- /dev/null
+++ b/.github/.gitignore
@@ -0,0 +1 @@
+*.html
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
new file mode 100644
index 0000000..bfc9f4d
--- /dev/null
+++ b/.github/workflows/pkgdown.yaml
@@ -0,0 +1,49 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown.yaml
+
+permissions: read-all
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/github-pages-deploy-action@v4.5.0
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs
diff --git a/.gitignore b/.gitignore
index b70a570..652758e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@ diego.txt
 .m
 .mat
 .csv
+docs
diff --git a/DESCRIPTION b/DESCRIPTION
index 07aa5d8..e350579 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -35,7 +35,8 @@ Imports:
     lifecycle,
     tictoc,
     reshape2
-URL: https://github.com/IvanWilli/DemoKin
+URL: https://github.com/IvanWilli/DemoKin,
+    https://ivanwilli.github.io/DemoKin/
 BugReports: https://github.com/IvanWilli/DemoKin/issues
 Depends: 
     R (>= 2.10)
diff --git a/_pkgdown.yml b/_pkgdown.yml
new file mode 100644
index 0000000..1a5902d
--- /dev/null
+++ b/_pkgdown.yml
@@ -0,0 +1,5 @@
+url: https://ivanwilli.github.io/DemoKin/
+template:
+  bootstrap: 5
+  bootswatch: cerulean
+

From 81933c4d85aacbc70f93d35f8579c1e4e06cf5dc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Iv=C3=A1n=20Williams?= <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 09:21:47 -0300
Subject: [PATCH 67/89] Revert "Updating function to allow user to select
 output years with the additional option of using abridged life tables"

---
 R/kin_multi_stage_time_variant_2sex.R         | 84 ++++++-------------
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 71 +++++++---------
 2 files changed, 59 insertions(+), 96 deletions(-)

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index 030e1b1..f52d3fa 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -20,10 +20,7 @@
 #' @param sex_Focal character. Female or Male as the user requests.
 #' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
 #' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
-#' @param model_years. The full timescale on which we run the matrix model. From these periods we extract the ``output_years''.
-#'                     Note that if we use abridged life-tables: e.g., 1960,1965,1970 to run the model, by default age_increment = 5
-#' @param age_year_consistent logical. Null sets age-bridge to be equal to year
-#' @param age_increment. numeric. If age_year_consisent FALSE set own age-gap
+#'
 #' @return A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
 
 #' @export
@@ -38,23 +35,17 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
                                               birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
                                               parity = FALSE,
                                               output_kin = NULL, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
-                                              summary_kin = TRUE, # Set to FALSE if we want a full age*stage distribution of kin
+                                              summary_kin = TRUE, # Set to FALSE if we want only a full age*stage distribution of kin
                                               sex_Focal = "Female", # Female Focal is default
                                               initial_stage_Focal = NULL,
-                                              output_years,
-                                              model_years,
-                                              age_year_consitent = TRUE,
-                                              age_increment = NULL){
+                                              output_years){
 
-  no_years <- (-1+length(U_list_females))
+  no_years <- length(U_list_females)
   na <- nrow(U_list_females[[1]])
   ns <- ncol(U_list_females[[1]])
-  if(age_year_consitent){age_increment <- as.numeric(model_years[2]-model_years[1])}
-  # Ensure inputs are lists of matrices and that the timescale same length
-
-  if(length(U_list_females) != (length(model_years))){stop("Proposed timescale longer than demographic timescale")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
-  if(output_years[length(output_years)] > model_years[length(model_years)]){stop("Output years longer than model run")}
 
+  # Ensure inputs are lists of matrices and that the timescale same length
+  # if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
   if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
   if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
@@ -87,8 +78,6 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
     total = no_years + 1, clear = FALSE, width = 60)
   tictoc::tic()
   for(year in 1:no_years){
-    pb$tick()
-
     T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
     T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
     T_f_list <- list()
@@ -274,20 +263,18 @@ kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
   kin_full <- create_full_dists_df(relative_data,
                                    relative_names,
                                    output_years,
-                                   model_years,
+                                   output_years[1],
                                    na,
                                    ns,
-                                   output_kin,
-                                   age_increment)
+                                   output_kin)
   if(summary_kin){
     kin_summary <- create_cumsum_df(relative_data,
-                                    relative_names,
-                                    output_years,
-                                    model_years,
-                                    na,
-                                    ns,
-                                    output_kin,
-                                    age_increment)
+                                relative_names,
+                                output_years,
+                                output_years[1],
+                                na,
+                                ns,
+                                output_kin)
     kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)}
   else{
     kin_out <- kin_full
@@ -716,36 +703,28 @@ all_kin_dy_TV <- function(Uf,
 create_cumsum_df <- function(kin_matrix_lists,
                              kin_names,
                              years,
-                             model_years,
+                             start_year,
                              na,
                              ns,
-                             specific_kin = NULL,
-                             increment = NULL){
-  if(length(years) > length(kin_matrix_lists[[1]])){stop("More years than data")}
-
-  matrix_model_time <- model_years
-  age_inc <- increment
-
+                             specific_kin = NULL){
   df_year_list <- list()
   for(j in years){
-    ii <- which(matrix_model_time == j)
+    ii <- as.numeric(j) - start_year + 1
     df_list <- list()
     for(i in 1 : length(kin_names)){
       kin_member <- kin_names[[i]]
       kin_data <- kin_matrix_lists[[i]]
       kin_data <- kin_data[[ii]]
       df <- as.data.frame(as.matrix(kin_data))
-      dims <- dim( kin_data )
+      dims <- dim( kin_data)
       nr <- dims[1]
       nc <- dims[2]
       female_kin <- df[1:(nr/2), 1:nc]
       male_kin <- df[ (1+nr/2) : nr, 1:nc]
-      colnames(female_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
-      colnames(male_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
       female_kin$stage <- rep(seq(1, ns), na)
       male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
-      male_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
       female_kin$Sex <- "Female"
       male_kin$Sex <- "Male"
       both_kin <- rbind(female_kin, male_kin)
@@ -757,7 +736,7 @@ create_cumsum_df <- function(kin_matrix_lists,
                                                 stage_kin = as.factor(stage),
                                                 count = num,
                                                 sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(paste(both_kin$age_focal))
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal)) - 1
       df <- both_kin
       df$year <- j
       df$group <- kin_member
@@ -791,20 +770,14 @@ create_cumsum_df <- function(kin_matrix_lists,
 create_full_dists_df <- function(kin_matrix_lists,
                                  kin_names,
                                  years,
-                                 model_years,
+                                 start_year,
                                  na,
                                  ns,
-                                 specific_kin = NULL,
-                                 increment = NULL){
-  if(length(years) > length(kin_matrix_lists[[1]])){stop("More years than data")}
-
-  matrix_model_time <- model_years
-  age_inc <- increment
-
+                                 specific_kin = NULL){
   df_year_list <- list()
   for(j in years){
+    ii <- as.numeric(j) - start_year + 1
     df_list <- list()
-    ii <- which(matrix_model_time == j)
     for(i in 1 : length(kin_names)){
       kin_member <- kin_names[[i]]
       kin_data <- kin_matrix_lists[[i]]
@@ -815,12 +788,10 @@ create_full_dists_df <- function(kin_matrix_lists,
       nc <- dims[2]
       female_kin <- df[1:(nr/2), 1:nc]
       male_kin <- df[ (1+nr/2) : nr, 1:nc]
-      colnames(female_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
-      colnames(male_kin) <- seq( 0 , age_inc*(na-1) , by = age_inc )
       female_kin$stage <- rep(seq(1, ns), na)
       male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
-      male_kin$age <- rep( seq( 0 , age_inc*(na-1) , by = age_inc ) , each = ns)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
       female_kin$Sex <- "Female"
       male_kin$Sex <- "Male"
       both_kin <- rbind(female_kin, male_kin)
@@ -830,7 +801,7 @@ create_full_dists_df <- function(kin_matrix_lists,
                          stage_kin = as.factor(stage),
                          count = value,
                          sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(paste(both_kin$age_focal))
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal))-1
       df <- both_kin
       df$year <- j
       df$group <- kin_member
@@ -838,7 +809,6 @@ create_full_dists_df <- function(kin_matrix_lists,
     }
     df_list <- do.call("rbind", df_list)
     df_year_list[[(1+length(df_year_list))]] <- df_list
-
   }
   df_year_list <- do.call("rbind", df_year_list)
   df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 34109df..e1295f8 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -14,16 +14,16 @@ knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
 library(devtools); load_all()
 ```
 
-Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model
-of kinship, there have been many extensions to the framework (many of which are documented within this package).
-Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates,
-Caswell [-@caswell_formal_2022] introduced two-sexes to the model,
-and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin.
-Here, we provide an R function which combines the three aforementioned models.
-
-In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks
-encompassing both sexes for an average member of a population, the sex of whom is user specified,
-and who is subject to time-varying demographic rates. We call this individual Focal.
+Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model 
+of kinship, there have been many extensions to the framework (many of which are documented within this package). 
+Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates, 
+Caswell [-@caswell_formal_2022] introduced two-sexes to the model, 
+and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin. 
+Here, we provide an R function which combines the three aforementioned models. 
+
+In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks 
+encompassing both sexes for an average member of a population, the sex of whom is user specified, 
+and who is subject to time-varying demographic rates. We call this individual Focal. 
 We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
 
 
@@ -35,9 +35,9 @@ options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise
 ```
 ### Kin counts by parity  ###
 
-In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from
-the [Human Mortality Database](https://www.mortality.org/)
-and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/).
+In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from 
+the [Human Mortality Database](https://www.mortality.org/) 
+and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). 
 Some simplifying assumptions we make due to data availability are as follows:
 
 i) Fertility rates vary with time, are distinct among parity class, but the same over sexes (the so-called ``androgynous approximation'').
@@ -59,16 +59,16 @@ This input list has length = the timescale, and each entry represents the rates
 This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
 
 5) `T_list_females` A list of lists of female age-specific probabilities of moving up parity over the timescale (in matrix forms).
-The outer list has length = the timescale. The inner list has length = number of ages.
-Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage.
+The outer list has length = the timescale. The inner list has length = number of ages. 
+Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage. 
 Thus for each year, we have a list of age-specific probabilities of moving from one stage to the next.
 
 6) Same as 5) but for males
 
-7) `H_list` A list of length = timescale, in which each element is a matrix which assigns the offspring of individuals in some stage to
+7) `H_list` A list of length = timescale, in which each element is a matrix which assigns the offspring of individuals in some stage to 
 the appropriate age class (age in rows and states in columns)
 
-To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed
+To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed 
 in another file and simply imported below. The code below reads in the above function input lists.
 
 ```{r eval=TRUE, message=FALSE, warning=FALSE, include=TRUE}
@@ -95,7 +95,7 @@ List starting 1965 ending 2022.
 
 F_mat_male == F_mat_fem.
 
-T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities
+T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities 
 a female moves up parity (inner list has length of number of age-classes).
 Outer list starting 1965 ending 2022
 
@@ -115,16 +115,14 @@ We feed the above inputs into the matrix model, along with other arguments:
 -   Focal born into parity 0 --> `initial_stage_Focal` = 1
 -   timescale as ouptut -- > `output_years` = c(1965, 1975, 1985, 1995, 2005)
 
+Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage 
+distribution of kin. 
 
-Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage
-distribution of kin.
-
-The first sets of time-varying vital rates in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965),
-
+The first sets of time-varying vital rates in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965), 
 the 41-st entry is U_mat_fem[[(1+40)]] (mortality in 2005). We require consistency between the length of the list of vital rates
 and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005). Therefore we use the input lists of demographic rates
 
-`U_list_females` = U_mat_fem[1:(1+no_years)] which runs from U_mat_fem[[1]] = 1965 set of rates, up to U_mat_fem[[41]] = 2005 set of rates,
+`U_list_females` = U_mat_fem[1:(1+no_years)] which runs from U_mat_fem[[1]] = 1965 set of rates, up to U_mat_fem[[41]] = 2005 set of rates, 
 and so on...
 
 > this run takes some time (round 10 min) so we don´t include the output in the vignette. Please try it!
@@ -148,11 +146,7 @@ kin_out_1965_2005 <-
                                     summary_kin = TRUE,
                                     sex_Focal = "Female", ##  define Focal's sex at birth
                                     initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    output_years = c(1965, 1975, 1985, 1995, 2005), ## the sequence of years we want output
-                                    model_years <- seq(1965, 2005), ## the sequence of years we model
-                                    age_year_consitent = TRUE, ## 
-                                    age_increment = NULL
-
+                                    output_years = c(1965, 1975, 1985, 1995, 2005) ## the sequence of years we run the function over
 )
 
 ```
@@ -163,7 +157,7 @@ kin_out_1965_2005 <-
 head(kin_out_1965_2005$kin_summary)
 ```
 
-Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin,
+Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin, 
 and produce the marginal stage distribution given age of Focal. We have a column corresponding to sex of kin `sex_kin`,
 a column showing which `year` we are considering, and a column headed `group` which selects the kin type.
 Finally, we have columns showing Focal's cohort of birth `cohort` (e.g., year - age of Focal), and an as.factor() equivalent.
@@ -171,13 +165,13 @@ Finally, we have columns showing Focal's cohort of birth `cohort` (e.g., year -
 
 ### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
 
-Let's suppose that we really want to understand the age*parity distributions of the accumulated number
-of aunts and uncles older than Focal's mother and father, for each age of Focal, over years 1965, 1975, 1985, 1995, 2005.
-Some people will do....
+Let's suppose that we really want to understand the age*parity distributions of the accumulated number 
+of aunts and uncles older than Focal's mother and father, for each age of Focal, over years 1965, 1975, 1985, 1995, 2005. 
+Some people will do.... 
 
 We restrict Focal's kinship network to aunts and uncles older than Focal's mother by `group` == "oa". We visualise the marginal
-parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different colour schemes. Implicit in the
-below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955,
+parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different colour schemes. Implicit in the 
+below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, 
 while a 40 year old Focal was born in 1965.
 
 ```{r, fig.height=6, fig.width=8}
@@ -206,7 +200,7 @@ kin_out_1965_2005$kin_summary %>%
 ```
 ### 1.1.2. Plotting the kin of Focal as a function of Focal's cohort of birth ####
 
-Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific.
+Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific. 
 We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring and only show Focals born of `cohort` 1910, 1925, or 1965:
 
 ```{r, fig.height = 6, fig.width = 8}
@@ -221,7 +215,7 @@ kin_out_1965_2005$kin_summary %>%
 ```
 The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
 
-The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by
+The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by 
 a well mixed parity-distribution at this age of Focal.
 
 the RHS plot (1965 cohort) simply reflects the fact that Focal will not start reproduction until around 15 years old.
@@ -261,8 +255,7 @@ kin_out_1965_2005$kin_full %>%
   ggplot2::ggtitle("Focal 50")
 ```
 
-
-Notice the discontinuity along the x-abscissa at 50. This reflects the fact that Focal's younger siblings
+Notice the discontinuity along the x-abscissa at 50. This reflects the fact that Focal's younger siblings 
 cannot are of age <50. Contrastingly, when we look at the age*stage distribution of older siblings, we observe another
 discontinuity which bounds kin to be of age >50, as plotted below:
 

From a289975344e857df7ee3f99bdc1bc176e4883e9c Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 09:45:00 -0300
Subject: [PATCH 68/89] web

---
 _pkgdown.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 1a5902d..8f6eda7 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,5 +1,4 @@
 url: https://ivanwilli.github.io/DemoKin/
 template:
   bootstrap: 5
-  bootswatch: cerulean
 

From 5a0c57106d81f14ec9ce49bdac853078dc55d405 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 10:10:09 -0300
Subject: [PATCH 69/89] remove devtools from vignettes

---
 vignettes/Reference_OneSex.Rmd                        | 2 +-
 vignettes/Reference_TwoSex.Rmd                        | 2 +-
 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
index ced906e..b6bd873 100644
--- a/vignettes/Reference_OneSex.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -13,7 +13,7 @@ vignette: >
 
 ```{r, eval = F, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
-library(devtools); load_all()
+# library(devtools); load_all()
 ```
 
 In this vignette, we'll demonstrate how `DemoKin` can be used to compute kinship networks for an average member of a given (female) population. Let us call her Focal: an average Swedish woman who has always lived in Sweden and whose family has never left the country.
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index 12289d9..fa9dd79 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -13,7 +13,7 @@ vignette: >
 
 ```{r, eval = T, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
-library(devtools); load_all()
+# library(devtools); load_all()
 ```
 
 Human males generally live shorter and reproduce later than females. 
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index e1295f8..1573908 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -11,7 +11,7 @@ vignette: >
 
 ```{r, eval = T, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
-library(devtools); load_all()
+# library(devtools); load_all()
 ```
 
 Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model 

From 555138f1b2a4f153a77bef6d1cb91f215c54d0a7 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 10:38:02 -0300
Subject: [PATCH 70/89] devtools inlcuding, removing library(DemoKin)

---
 vignettes/Reference_TwoSex.Rmd                        | 3 ++-
 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd | 3 +--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index fa9dd79..0c4c868 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -26,11 +26,12 @@ The function `kin2sex` implements two-sex kinship models as introduced by Caswel
 This vignette show how to run two-sex models and highlights some of the advantages of this model over one-sex models in populations with time-invariant and time-variant rates.
 
 ```{r, message=FALSE, warning=FALSE}
-library(DemoKin)
+# library(DemoKin)
 library(tidyr)
 library(dplyr)
 library(ggplot2)
 library(knitr)
+devtools::load_all()
 ```
 
 ### 1. Demographic rates by sex
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index 1573908..c67b897 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -11,7 +11,7 @@ vignette: >
 
 ```{r, eval = T, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
-# library(devtools); load_all()
+devtools::load_all()
 ```
 
 Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model 
@@ -28,7 +28,6 @@ We seek the number of, age, and stage distribution of Focal's relatives, for eac
 
 
 ```{r}
-# library(DemoKin)
 library(Matrix)
 library(tictoc)
 options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise output (but also #lose #progress bar)

From 566a5cfcdd6af9547ac5c788eb5dc07bd15adfb8 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 11:33:13 -0300
Subject: [PATCH 71/89] add pkgdown

---
 DESCRIPTION                                           | 3 ++-
 vignettes/Reference_OneSex.Rmd                        | 2 +-
 vignettes/Reference_TwoSex.Rmd                        | 5 ++++-
 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd | 3 ++-
 4 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index e350579..250613d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -18,7 +18,8 @@ Suggests:
     knitr,
     rmarkdown,
     testthat (>= 3.0.0),
-    ggplot2
+    ggplot2,
+    pkgdown
 VignetteBuilder: knitr
 Imports: 
     dplyr,
diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
index b6bd873..16662a5 100644
--- a/vignettes/Reference_OneSex.Rmd
+++ b/vignettes/Reference_OneSex.Rmd
@@ -26,7 +26,7 @@ First, we compute kin counts in a **time-invariant** framework. We assume that F
 In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of survival ratios and another vector of fertility rates. In this example, we get the data for the year 2015, and run the matrix models:
 
 ```{r, message=FALSE, warning=FALSE}
-library(DemoKin)
+pkgload::load_all()
 library(tidyr)
 library(dplyr)
 library(ggplot2)
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
index 0c4c868..0c0064b 100644
--- a/vignettes/Reference_TwoSex.Rmd
+++ b/vignettes/Reference_TwoSex.Rmd
@@ -31,7 +31,8 @@ library(tidyr)
 library(dplyr)
 library(ggplot2)
 library(knitr)
-devtools::load_all()
+pkgload::load_all()
+# devtools::load_all()
 ```
 
 ### 1. Demographic rates by sex
@@ -41,6 +42,8 @@ For this example, we use data from 2012 France to exemplify the use of the two-s
 Data on female and male fertility and mortality are included in `DemoKin`. In this population, male and female TFR is almost identical (1.98 and 1.99) but the distributions of fertility by sex varies over age:
 
 ```{r}
+data(fra_asfr_sex, package = "DemoKin")
+data(fra_surv_sex, package = "DemoKin")
 fra_fert_f <- fra_asfr_sex[,"ff"]
 fra_fert_m <- fra_asfr_sex[,"fm"]
 fra_surv_f <- fra_surv_sex[,"pf"]
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
index c67b897..c519820 100644
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
@@ -11,7 +11,7 @@ vignette: >
 
 ```{r, eval = T, include=FALSE}
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
-devtools::load_all()
+pkgload::load_all()
 ```
 
 Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model 
@@ -28,6 +28,7 @@ We seek the number of, age, and stage distribution of Focal's relatives, for eac
 
 
 ```{r}
+pkgload::load_all()
 library(Matrix)
 library(tictoc)
 options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise output (but also #lose #progress bar)

From 8e53ca5e42ac65d4d9c785267ddf49c7dca97bea Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 16:13:37 -0300
Subject: [PATCH 72/89] prepare using demokin-web

---
 R/data.R                                      |  56 ++
 _pkgdown.yml                                  |  46 ++
 data/F_mat_fem_edu.rda                        | Bin 0 -> 3443 bytes
 data/F_mat_male_edu.rda                       | Bin 0 -> 3445 bytes
 data/H_mat_edu.rda                            | Bin 0 -> 330 bytes
 data/T_mat_fem_edu.rda                        | Bin 0 -> 1854 bytes
 data/T_mat_male_edu.rda                       | Bin 0 -> 1861 bytes
 data/U_mat_fem_edu.rda                        | Bin 0 -> 3511 bytes
 data/U_mat_male_edu.rda                       | Bin 0 -> 3656 bytes
 docs/404.html                                 | 132 ++--
 docs/LICENSE-text.html                        | 109 ++-
 docs/LICENSE.html                             | 109 ++-
 docs/authors.html                             | 133 ++--
 docs/index.html                               | 127 ++--
 docs/pkgdown.js                               | 184 +++--
 docs/pkgdown.yml                              |  14 +-
 man/F_mat_fem_edu.Rd                          |  19 +
 man/F_mat_male_edu.Rd                         |  19 +
 man/H_mat_edu.Rd                              |  19 +
 man/T_mat_fem_edu.Rd                          |  19 +
 man/T_mat_male_edu.Rd                         |  19 +
 man/U_mat_fem_edu.Rd                          |  19 +
 man/U_mat_male_edu.Rd                         |  19 +
 vignettes/1_1_OneSex_TimeInvariant_Age.Rmd    | 484 +++++++++++++
 vignettes/1_2_OneSex_TimeVarying_Age.Rmd      | 319 +++++++++
 vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd    | 360 ++++++++++
 vignettes/1_4_TwoSex_TimeVarying_Age.Rmd      | 435 ++++++++++++
 .../2_1_OneSex_TimeInvariant_AgeStage.Rmd     | 315 +++++++++
 vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd | 661 ++++++++++++++++++
 vignettes/Reference_OneSex.Rmd                | 270 -------
 vignettes/Reference_TwoSex.Rmd                | 329 ---------
 ...eference_TwoSex_MultiState_TimeVariant.Rmd | 292 --------
 vignettes/references.bib                      |   8 +
 33 files changed, 3177 insertions(+), 1339 deletions(-)
 create mode 100644 data/F_mat_fem_edu.rda
 create mode 100644 data/F_mat_male_edu.rda
 create mode 100644 data/H_mat_edu.rda
 create mode 100644 data/T_mat_fem_edu.rda
 create mode 100644 data/T_mat_male_edu.rda
 create mode 100644 data/U_mat_fem_edu.rda
 create mode 100644 data/U_mat_male_edu.rda
 create mode 100644 man/F_mat_fem_edu.Rd
 create mode 100644 man/F_mat_male_edu.Rd
 create mode 100644 man/H_mat_edu.Rd
 create mode 100644 man/T_mat_fem_edu.Rd
 create mode 100644 man/T_mat_male_edu.Rd
 create mode 100644 man/U_mat_fem_edu.Rd
 create mode 100644 man/U_mat_male_edu.Rd
 create mode 100644 vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
 create mode 100644 vignettes/1_2_OneSex_TimeVarying_Age.Rmd
 create mode 100644 vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
 create mode 100644 vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
 create mode 100644 vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
 create mode 100644 vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
 delete mode 100644 vignettes/Reference_OneSex.Rmd
 delete mode 100644 vignettes/Reference_TwoSex.Rmd
 delete mode 100644 vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd

diff --git a/R/data.R b/R/data.R
index 156b74c..7a4db6b 100644
--- a/R/data.R
+++ b/R/data.R
@@ -1,3 +1,59 @@
+#' Singapore: List of matrices that redistribute newborns to age-class 1 and "no education" category
+#' @docType data
+#' @format
+#'   The data is aggregated into 5-year age groups and 5-year time intervals
+#' @source
+#'   Wittgenstein Center
+"H_mat_edu"
+
+#' Singapore: Lists of transition matrices showing probabilities of moving between education states. Females
+#' @docType data
+#' @format
+#'   The data is aggregated into 5-year age groups and 5-year time intervals
+#' @source
+#'   Wittgenstein Center
+"T_mat_fem_edu"
+
+#' Singapore: Lists of transition matrices showing probabilities of moving between education states. Males
+#' @docType data
+#' @format
+#'   The data is aggregated into 5-year age groups and 5-year time intervals
+#' @source
+#'   Wittgenstein Center
+"T_mat_male_edu"
+
+#' Singapore: Lists of matrices containing fertility rates by age and education. Females
+#' @docType data
+#' @format
+#'   The data is aggregated into 5-year age groups and 5-year time intervals
+#' @source
+#'   Wittgenstein Center
+"F_mat_fem_edu"
+
+#' Singapore: Lists of matrices containing fertility rates by age and education. Males
+#' @docType data
+#' @format
+#'   The data is aggregated into 5-year age groups and 5-year time intervals
+#' @source
+#'   Wittgenstein Center
+"F_mat_male_edu"
+
+#' Singapore: Lists of matrices containing survival probabilities by age (rows) and education (columns) from 2020-2090. Females
+#' @docType data
+#' @format
+#'   The data is aggregated into 5-year age groups and 5-year time intervals
+#' @source
+#'   Wittgenstein Center
+"U_mat_fem_edu"
+
+#' Singapore: Lists of matrices containing survival probabilities by age (rows) and education (columns) from 2020-2090. Males
+#' @docType data
+#' @format
+#'   The data is aggregated into 5-year age groups and 5-year time intervals
+#' @source
+#'   Wittgenstein Center
+"U_mat_male_edu"
+
 #' UK female fertility from 1965 to 2022
 #' @docType data
 #' @format
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 8f6eda7..eaa2767 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,4 +1,50 @@
 url: https://ivanwilli.github.io/DemoKin/
 template:
   bootstrap: 5
+  bootswatch: flatly
+  bslib:
+    primary: "#0054AD"
+    border-radius: 0.5rem
+    btn-border-radius: 0.25rem
 
+navbar:
+  structure:
+    left: [home, age_models, stage_models]
+    right: [github]
+  components:
+    home:
+      icon: fas fa-home fa-lg
+      href: index.html
+    age_models:
+      text: "Models by Age"
+      menu:
+      - text: "One-sex time-invariant model"
+        href: articles/1_1_OneSex_TimeInvariant_Age.html
+      - text: "One-sex time-varying model"
+        href: articles/1_2_OneSex_TimeVarying_Age.html
+      - text: "Two-sex time-invariant model"
+        href: articles/1_3_TwoSex_TimeInvariant_Age.html
+      - text: "Two-sex time-varying model"
+        href: articles/1_4_TwoSex_TimeVarying_Age.html
+    stage_models:
+      text: "Models by Age and Stage"
+      menu:
+      - text: "One-sex time-invariant model"
+        href: articles/2_1_OneSex_TimeInvariant_AgeStage.html
+      - text: "Two-sex time-varying model"
+        href: articles/2_2_TwoSex_TimeVarying_AgeStage.html
+    github:
+      icon: fab fa-github fa-lg
+      href: https://github.com/IvanWilli/DemoKin
+
+articles:
+  - title: "Models by Age"
+    contents:
+      - 1_1_OneSex_TimeInvariant_Age
+      - 1_2_OneSex_TimeVarying_Age
+      - 1_3_TwoSex_TimeInvariant_Age
+      - 1_4_TwoSex_TimeVarying_Age
+  - title: "Models by Age and Stage"
+    contents:
+      - 2_1_OneSex_TimeInvariant_AgeStage
+      - 2_2_TwoSex_TimeVarying_AgeStage
diff --git a/data/F_mat_fem_edu.rda b/data/F_mat_fem_edu.rda
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+<li class="nav-item"><a class="nav-link" href="https://ivanwilli.github.io/DemoKin/index.html"><span class="fa fas fa-home fa-lg"></span></a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
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+<li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
+    <li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage">
+<li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
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 </div>
 
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diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index aa823db..606ba6b 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -1,91 +1,62 @@
 <!DOCTYPE html>
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-        <a class="navbar-link" href="index.html">DemoKin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • DemoKin</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"></head><body>
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-      <a href="articles/Reference_TwoSex_MultiState_TimeVariant.html">Expected kin counts by type of relative in a two-sex multi-state time-varying framework</a>
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
 
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
 
 
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
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+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age"><li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
+    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
+  </ul></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage"><li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
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+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
       <h1>License</h1>
+
     </div>
 
 <pre>YEAR: 2021
 COPYRIGHT HOLDER: DemoKin authors
 </pre>
 
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-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
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+  </main></div>
 
 
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-      <footer><div class="copyright">
-  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
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diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index 7222755..102b7d2 100644
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@@ -1,65 +1,44 @@
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-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>MIT License • DemoKin</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="MIT License"></head><body>
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+    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
+
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age"><li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
+    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
+  </ul></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage"><li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
 
 
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+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
 
-      </header><div class="row">
-  <div class="contents col-md-9">
-    <div class="page-header">
       <h1>MIT License</h1>
+      <small class="dont-index">Source: <a href="https://github.com/IvanWilli/DemoKin/blob/HEAD/LICENSE.md" class="external-link"><code>LICENSE.md</code></a></small>
     </div>
 
 <div id="mit-license" class="section level1">
@@ -70,26 +49,18 @@ <h1>MIT License</h1>
 <p>THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
 </div>
 
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-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
-    </nav></div>
-
-</div>
-
+  </main></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
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diff --git a/docs/authors.html b/docs/authors.html
index 7b85e3a..f77dc27 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,68 +1,47 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Authors and Citation • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-<![endif]--></head><body data-spy="scroll" data-target="#toc">
-
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-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="index.html">DemoKin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
-      </span>
-    </div>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • DemoKin</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"></head><body>
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-    Articles
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-      <a href="articles/Reference_OneSex.html">Expected kin counts by type of relative in a one-sex framework</a>
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-      <a href="articles/Reference_TwoSex.html">Two-sex kinship model</a>
-    </li>
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-      <a href="articles/Reference_TwoSex_MultiState_TimeVariant.html">Expected kin counts by type of relative in a two-sex multi-state time-varying framework</a>
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age"><li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
+    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
+  </ul></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage"><li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
+  </ul></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
+      </ul></div>
 
 
+  </div>
+</nav><div class="container template-citation-authors">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
 
-      </header><div class="row">
-  <div class="contents col-md-9">
-    <div class="section level2 authors-section">
-      <div class="page-header">
-        <h1>Authors and Citation</h1>
-      </div>
+      <h1>Authors and Citation</h1>
+    </div>
 
+    <div class="section level2">
+      <h2>Authors</h2>
 
       <ul class="list-unstyled"><li>
           <p><strong>Iván Williams</strong>. Maintainer.
@@ -81,42 +60,38 @@ <h1>Authors and Citation</h1>
           </p>
         </li>
       </ul></div>
-    <div class="section level2 citation-section">
-    <div>
-      <h1 id="citation">Citation</h1>
-      <small class="dont-index">Source: <a href="https://github.com/IvanWilli/DemoKin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small>
-    </div>
-    </div>
 
+    <div class="section level2">
+      <h2 id="citation">Citation</h2>
+      <p><small class="dont-index">Source: <a href="https://github.com/IvanWilli/DemoKin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-    <p>Alburez-Gutierrez D (2025).
+      <p>Alburez-Gutierrez D (2025).
 <em>DemoKin: Estimate Population Kin Distribution</em>.
-R package version 1.0.3, <a href="https://github.com/IvanWilli/DemoKin" class="external-link">https://github.com/IvanWilli/DemoKin</a>.
+https://github.com/IvanWilli/DemoKin,
+https://ivanwilli.github.io/DemoKin/.
 </p>
-    <pre>@Manual{,
+      <pre>@Manual{,
   title = {DemoKin: Estimate Population Kin Distribution},
   author = {Diego Alburez-Gutierrez},
   year = {2025},
-  note = {R package version 1.0.3},
-  url = {https://github.com/IvanWilli/DemoKin},
+  note = {https://github.com/IvanWilli/DemoKin,
+    https://ivanwilli.github.io/DemoKin/},
 }</pre>
+    </div>
 
-  </div>
-
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+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
-      <footer><div class="copyright">
-  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
 </div>
 
-      </footer></div>
-
+    </footer></div>
 
 
 
diff --git a/docs/index.html b/docs/index.html
index 45632ea..6bba705 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -4,86 +4,61 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <title>Estimate Population Kin Distribution • DemoKin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous">
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-<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
-<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
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-<script src="pkgdown.js"></script><meta property="og:title" content="Estimate Population Kin Distribution">
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Estimate Population Kin Distribution">
+<meta name="description" content="Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.">
 <meta property="og:description" content="Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.">
-<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
-<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
-<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
-<![endif]-->
 </head>
-<body data-spy="scroll" data-target="#toc">
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
-    <div class="container template-home">
-      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
-  <div class="container">
-    <div class="navbar-header">
-      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
-        <span class="sr-only">Toggle navigation</span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-        <span class="icon-bar"></span>
-      </button>
-      <span class="navbar-brand">
-        <a class="navbar-link" href="index.html">DemoKin</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
-      </span>
-    </div>
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
 
-    <div id="navbar" class="navbar-collapse collapse">
-      <ul class="nav navbar-nav">
-<li>
-  <a href="reference/index.html">Reference</a>
-</li>
-<li class="dropdown">
-  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
-    Articles
+    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
 
-    <span class="caret"></span>
-  </a>
-  <ul class="dropdown-menu" role="menu">
-<li>
-      <a href="articles/Reference_OneSex.html">Expected kin counts by type of relative in a one-sex framework</a>
-    </li>
-    <li>
-      <a href="articles/Reference_TwoSex.html">Two-sex kinship model</a>
-    </li>
-    <li>
-      <a href="articles/Reference_TwoSex_MultiState_TimeVariant.html">Expected kin counts by type of relative in a two-sex multi-state time-varying framework</a>
-    </li>
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="active nav-item"><a class="nav-link" href="index.html"><span class="fa fas fa-home fa-lg"></span></a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age">
+<li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
+    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
   </ul>
 </li>
-<li>
-  <a href="news/index.html">Changelog</a>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage">
+<li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
+    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
+  </ul>
 </li>
       </ul>
-<ul class="nav navbar-nav navbar-right">
-<li>
-  <a href="https://github.com/IvanWilli/DemoKin/" class="external-link">
-    <span class="fab fa-github fa-lg"></span>
-
-  </a>
-</li>
+<ul class="navbar-nav">
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
       </ul>
 </div>
-<!--/.nav-collapse -->
-  </div>
-<!--/.container -->
-</div>
-<!--/.navbar -->
-
 
 
-      </header><div class="row">
-  <div class="contents col-md-9">
-<div class="section level1">
+  </div>
+</nav><div class="container template-home">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="section level1">
 <div class="page-header"><h1 id="demokin">DemoKin<a class="anchor" aria-label="anchor" href="#demokin"></a>
 </h1></div>
 <div class="columns">
@@ -258,7 +233,7 @@ <h2 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a>
 <div class="section level2">
 <h2 id="vignette">Vignette<a class="anchor" aria-label="anchor" href="#vignette"></a>
 </h2>
-<p>For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html" class="external-link">Reference_OneSex</a> vignette; also accessible from DemoKin: <code><a href="articles/Reference_OneSex.html">vignette("Reference_OneSex", package = "DemoKin")</a></code>. For two-sex models, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html" class="external-link">Reference_TwoSex</a> vignette; also accessible from DemoKin: <code><a href="articles/Reference_TwoSex.html">vignette("Reference_TwoSex", package = "DemoKin")</a></code>. If the vignette does not load, you may need to install the package as <code>devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)</code>.</p>
+<p>For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html" class="external-link">Reference_OneSex</a> vignette; also accessible from DemoKin: <code><a href="https://ivanwilli.github.io/DemoKin/articles/Reference_OneSex.html">vignette("Reference_OneSex", package = "DemoKin")</a></code>. For two-sex models, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html" class="external-link">Reference_TwoSex</a> vignette; also accessible from DemoKin: <code><a href="https://ivanwilli.github.io/DemoKin/articles/Reference_TwoSex.html">vignette("Reference_TwoSex", package = "DemoKin")</a></code>. If the vignette does not load, you may need to install the package as <code>devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)</code>.</p>
 </div>
 <div class="section level2">
 <h2 id="citation">Citation<a class="anchor" aria-label="anchor" href="#citation"></a>
@@ -277,10 +252,7 @@ <h2 id="get-involved">Get involved!<a class="anchor" aria-label="anchor" href="#
 <!-- ## References -->
 </div>
 </div>
-  </div>
-
-  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
-    <div class="links">
+  </main><aside class="col-md-3"><div class="links">
 <h2 data-toc-skip>Links</h2>
 <ul class="list-unstyled">
 <li><a href="https://cloud.r-project.org/package=DemoKin" class="external-link">View on CRAN</a></li>
@@ -316,27 +288,24 @@ <h2 data-toc-skip>Developers</h2>
 
 
 
-  </div>
+  </aside>
 </div>
 
 
-      <footer><div class="copyright">
-  <p></p>
-<p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown">
-  <p></p>
-<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
 </div>
 
-      </footer>
+    </footer>
 </div>
 
 
 
 
 
-
   </body>
 </html>
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index 6f0eee4..1a99c65 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -2,83 +2,43 @@
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-    $('.navbar-fixed-top').headroom();
+    $('nav.navbar').headroom();
 
-    $('body').css('padding-top', $('.navbar').height() + 10);
-    $(window).resize(function(){
-      $('body').css('padding-top', $('.navbar').height() + 10);
+    Toc.init({
+      $nav: $("#toc"),
+      $scope: $("main h2, main h3, main h4, main h5, main h6")
     });
 
-    $('[data-toggle="tooltip"]').tooltip();
-
-    var cur_path = paths(location.pathname);
-    var links = $("#navbar ul li a");
-    var max_length = -1;
-    var pos = -1;
-    for (var i = 0; i < links.length; i++) {
-      if (links[i].getAttribute("href") === "#")
-        continue;
-      // Ignore external links
-      if (links[i].host !== location.host)
-        continue;
-
-      var nav_path = paths(links[i].pathname);
-
-      var length = prefix_length(nav_path, cur_path);
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-      menu_anchor.parent().addClass("active");
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-
-  function paths(pathname) {
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-
-    var end = pieces[pieces.length - 1];
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-      return(-1);
-
-    // Special case for length-0 haystack, since for loop won't run
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-      return(needle.length === 0 ? 0 : -1);
+    if ($('#toc').length) {
+      $('body').scrollspy({
+        target: '#toc',
+        offset: $("nav.navbar").outerHeight() + 1
+      });
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-    for (var i = 0; i < haystack.length; i++) {
-      if (needle[i] != haystack[i])
-        return(i);
-    }
+    // Activate popovers
+    $('[data-bs-toggle="popover"]').popover({
+      container: 'body',
+      html: true,
+      trigger: 'focus',
+      placement: "top",
+      sanitize: false,
+    });
 
-    return(haystack.length);
-  }
+    $('[data-bs-toggle="tooltip"]').tooltip();
 
   /* Clipboard --------------------------*/
 
   function changeTooltipMessage(element, msg) {
-    var tooltipOriginalTitle=element.getAttribute('data-original-title');
-    element.setAttribute('data-original-title', msg);
+    var tooltipOriginalTitle=element.getAttribute('data-bs-original-title');
+    element.setAttribute('data-bs-original-title', msg);
     $(element).tooltip('show');
-    element.setAttribute('data-original-title', tooltipOriginalTitle);
+    element.setAttribute('data-bs-original-title', tooltipOriginalTitle);
   }
 
   if(ClipboardJS.isSupported()) {
     $(document).ready(function() {
-      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
+      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
 
       $("div.sourceCode").addClass("hasCopyButton");
 
@@ -89,20 +49,114 @@
       $('.btn-copy-ex').tooltip({container: 'body'});
 
       // Initialize clipboard:
-      var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
+      var clipboard = new ClipboardJS('[data-clipboard-copy]', {
         text: function(trigger) {
           return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
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       });
 
-      clipboardBtnCopies.on('success', function(e) {
+      clipboard.on('success', function(e) {
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         e.clearSelection();
       });
 
-      clipboardBtnCopies.on('error', function() {
+      clipboard.on('error', function(e) {
         changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
       });
+
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   }
+
+    /* Search marking --------------------------*/
+    var url = new URL(window.location.href);
+    var toMark = url.searchParams.get("q");
+    var mark = new Mark("main#main");
+    if (toMark) {
+      mark.mark(toMark, {
+        accuracy: {
+          value: "complementary",
+          limiters: [",", ".", ":", "/"],
+        }
+      });
+    }
+
+  /* Search --------------------------*/
+  /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */
+    // Initialise search index on focus
+  var fuse;
+  $("#search-input").focus(async function(e) {
+    if (fuse) {
+      return;
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+
+    $(e.target).addClass("loading");
+    var response = await fetch($("#search-input").data("search-index"));
+    var data = await response.json();
+
+    var options = {
+      keys: ["what", "text", "code"],
+      ignoreLocation: true,
+      threshold: 0.1,
+      includeMatches: true,
+      includeScore: true,
+    };
+    fuse = new Fuse(data, options);
+
+    $(e.target).removeClass("loading");
+  });
+
+  // Use algolia autocomplete
+  var options = {
+    autoselect: true,
+    debug: true,
+    hint: false,
+    minLength: 2,
+  };
+  var q;
+async function searchFuse(query, callback) {
+  await fuse;
+
+  var items;
+  if (!fuse) {
+    items = [];
+  } else {
+    q = query;
+    var results = fuse.search(query, { limit: 20 });
+    items = results
+      .filter((x) => x.score <= 0.75)
+      .map((x) => x.item);
+    if (items.length === 0) {
+      items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}];
+    }
+  }
+  callback(items);
+}
+  $("#search-input").autocomplete(options, [
+    {
+      name: "content",
+      source: searchFuse,
+      templates: {
+        suggestion: (s) => {
+          if (s.title == s.what) {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div>`;
+          } else if (s.previous_headings == "") {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.what}`;
+          } else {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.previous_headings} > ${s.what}`;
+          }
+        },
+      },
+    },
+  ]).on('autocomplete:selected', function(event, s) {
+    window.location.href = s.path + "?q=" + q + "#" + s.id;
+  });
+  });
 })(window.jQuery || window.$)
+
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
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+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 81ed8a8..789ebab 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -2,7 +2,13 @@ pandoc: 3.1.1
 pkgdown: 2.1.1.9000
 pkgdown_sha: 62d5c5a0fc12899aae97f6ce14018d2784bf308b
 articles:
-  Reference_OneSex: Reference_OneSex.html
-  Reference_TwoSex: Reference_TwoSex.html
-  Reference_TwoSex_MultiState_TimeVariant: Reference_TwoSex_MultiState_TimeVariant.html
-last_built: 2025-04-23T23:39Z
+  1_1_OneSex_TimeInvariant_Age: 1_1_OneSex_TimeInvariant_Age.html
+  1_2_OneSex_TimeVarying_Age: 1_2_OneSex_TimeVarying_Age.html
+  1_3_TwoSex_TimeInvariant_Age: 1_3_TwoSex_TimeInvariant_Age.html
+  1_4_TwoSex_TimeVarying_Age: 1_4_TwoSex_TimeVarying_Age.html
+  2_1_OneSex_TimeInvariant_AgeStage: 2_1_OneSex_TimeInvariant_AgeStage.html
+  2_2_TwoSex_TimeVarying_AgeStage: 2_2_TwoSex_TimeVarying_AgeStage.html
+last_built: 2025-04-24T17:44Z
+urls:
+  reference: https://ivanwilli.github.io/DemoKin/reference
+  article: https://ivanwilli.github.io/DemoKin/articles
diff --git a/man/F_mat_fem_edu.Rd b/man/F_mat_fem_edu.Rd
new file mode 100644
index 0000000..73d840e
--- /dev/null
+++ b/man/F_mat_fem_edu.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{F_mat_fem_edu}
+\alias{F_mat_fem_edu}
+\title{Singapore: Lists of matrices containing fertility rates by age and education. Females}
+\format{
+The data is aggregated into 5-year age groups and 5-year time intervals
+}
+\source{
+Wittgenstein Center
+}
+\usage{
+F_mat_fem_edu
+}
+\description{
+Singapore: Lists of matrices containing fertility rates by age and education. Females
+}
+\keyword{datasets}
diff --git a/man/F_mat_male_edu.Rd b/man/F_mat_male_edu.Rd
new file mode 100644
index 0000000..88e10bb
--- /dev/null
+++ b/man/F_mat_male_edu.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{F_mat_male_edu}
+\alias{F_mat_male_edu}
+\title{Singapore: Lists of matrices containing fertility rates by age and education. Males}
+\format{
+The data is aggregated into 5-year age groups and 5-year time intervals
+}
+\source{
+Wittgenstein Center
+}
+\usage{
+F_mat_male_edu
+}
+\description{
+Singapore: Lists of matrices containing fertility rates by age and education. Males
+}
+\keyword{datasets}
diff --git a/man/H_mat_edu.Rd b/man/H_mat_edu.Rd
new file mode 100644
index 0000000..f800037
--- /dev/null
+++ b/man/H_mat_edu.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{H_mat_edu}
+\alias{H_mat_edu}
+\title{Singapore: List of matrices that redistribute newborns to age-class 1 and "no education" category}
+\format{
+The data is aggregated into 5-year age groups and 5-year time intervals
+}
+\source{
+Wittgenstein Center
+}
+\usage{
+H_mat_edu
+}
+\description{
+Singapore: List of matrices that redistribute newborns to age-class 1 and "no education" category
+}
+\keyword{datasets}
diff --git a/man/T_mat_fem_edu.Rd b/man/T_mat_fem_edu.Rd
new file mode 100644
index 0000000..8209920
--- /dev/null
+++ b/man/T_mat_fem_edu.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{T_mat_fem_edu}
+\alias{T_mat_fem_edu}
+\title{Singapore: Lists of transition matrices showing probabilities of moving between education states. Females}
+\format{
+The data is aggregated into 5-year age groups and 5-year time intervals
+}
+\source{
+Wittgenstein Center
+}
+\usage{
+T_mat_fem_edu
+}
+\description{
+Singapore: Lists of transition matrices showing probabilities of moving between education states. Females
+}
+\keyword{datasets}
diff --git a/man/T_mat_male_edu.Rd b/man/T_mat_male_edu.Rd
new file mode 100644
index 0000000..0bab26d
--- /dev/null
+++ b/man/T_mat_male_edu.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{T_mat_male_edu}
+\alias{T_mat_male_edu}
+\title{Singapore: Lists of transition matrices showing probabilities of moving between education states. Males}
+\format{
+The data is aggregated into 5-year age groups and 5-year time intervals
+}
+\source{
+Wittgenstein Center
+}
+\usage{
+T_mat_male_edu
+}
+\description{
+Singapore: Lists of transition matrices showing probabilities of moving between education states. Males
+}
+\keyword{datasets}
diff --git a/man/U_mat_fem_edu.Rd b/man/U_mat_fem_edu.Rd
new file mode 100644
index 0000000..f1a6255
--- /dev/null
+++ b/man/U_mat_fem_edu.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{U_mat_fem_edu}
+\alias{U_mat_fem_edu}
+\title{Singapore: Lists of matrices containing survival probabilities by age (rows) and education (columns) from 2020-2090. Females}
+\format{
+The data is aggregated into 5-year age groups and 5-year time intervals
+}
+\source{
+Wittgenstein Center
+}
+\usage{
+U_mat_fem_edu
+}
+\description{
+Singapore: Lists of matrices containing survival probabilities by age (rows) and education (columns) from 2020-2090. Females
+}
+\keyword{datasets}
diff --git a/man/U_mat_male_edu.Rd b/man/U_mat_male_edu.Rd
new file mode 100644
index 0000000..e67777b
--- /dev/null
+++ b/man/U_mat_male_edu.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{U_mat_male_edu}
+\alias{U_mat_male_edu}
+\title{Singapore: Lists of matrices containing survival probabilities by age (rows) and education (columns) from 2020-2090. Males}
+\format{
+The data is aggregated into 5-year age groups and 5-year time intervals
+}
+\source{
+Wittgenstein Center
+}
+\usage{
+U_mat_male_edu
+}
+\description{
+Singapore: Lists of matrices containing survival probabilities by age (rows) and education (columns) from 2020-2090. Males
+}
+\keyword{datasets}
diff --git a/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd b/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
new file mode 100644
index 0000000..c17b222
--- /dev/null
+++ b/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
@@ -0,0 +1,484 @@
+---
+title: "One-sex time-invariant kinship model specified by age"
+output:
+  html_document:
+    toc: true
+    toc_float: 
+      collapsed: false
+      smooth_scroll: true
+    theme: readable
+    highlight: pygments
+    number_sections: true
+    code_folding: show
+    df_print: paged
+    fig_caption: true
+bibliography: references.bib
+vignette: >
+  %\VignetteIndexEntry{One-sex time-invariant kinship model specified by age}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include=FALSE}
+# Set up code chunk options
+knitr::opts_chunk$set(echo = TRUE,
+                      message = FALSE, 
+                      warning = FALSE, 
+                      fig.align = 'center',
+                      fig.width = 8,
+                      fig.height = 6,
+                      dpi = 300)
+# Prevent scientific notation (useful for the rate calculation)
+options(scipen = 999999)
+pkgload::load_all()
+```
+
+<style>
+/* Make TOC appear on the right side */
+.tocify {
+  position: fixed;
+  top: 50px;
+  right: 0;
+  width: 240px;
+  height: calc(100% - 50px);
+  overflow-y: auto;
+  margin-left: 0;
+  padding-left: 0;
+}
+
+/* Adjust main content to make room for right-side TOC */
+.main-container {
+  max-width: calc(100% - 260px);
+  margin-left: 10px;
+  margin-right: 250px;
+}
+
+/* Limit TOC to only show 2 levels */
+.tocify-subheader .tocify-subheader {
+  display: none;
+}
+
+/* Style for learning objectives box */
+.learning-objectives {
+  background-color: #e0f7fa;
+  border-left: 5px solid #00acc1;
+  padding: 15px;
+  margin-bottom: 20px;
+  border-radius: 3px;
+}
+</style>
+
+<div class="learning-objectives">
+<strong>Learning Objectives</strong>: In this tutorial, you will learn how to use the DemoKin package to analyze kinship networks, understand the mechanics of one-sex time-invariant models, and visualize kinship dynamics across the life course.
+</div>
+
+# Introduction {#introduction}
+
+Kinship is a fundamental property of human populations and a key form of social structure. Demographers have long been interested in the interplay between demographic change and family configuration. This has led to the development of sophisticated methodological and conceptual approaches for the study of kinship, some of which are explored in this tutorial.
+
+Kinship analysis can answer a range of important questions:
+
+- How many relatives might people have at different ages, and what is the age distribution of these relatives?
+- How does family structure (both the number and age distribution of kin) evolve as populations undergo demographic transition?
+
+In this tutorial, we will implement matrix kinship models using the `DemoKin` package to calculate kin counts and age distributions. We begin with the simplest model: a **time-invariant one-sex model**, outlined in Caswell [-@caswell_formal_2019]. In this model, we assume that everyone in the population experiences the same mortality and fertility rates throughout their lives (e.g., the 2015 rates), and we only trace female kin relationships.
+
+## Preparation {#preparation}
+
+Before starting the session, please ensure you complete the following preparatory steps:
+
+1. If you haven't already, install R and RStudio. This is a useful tutorial: https://rstudio-education.github.io/hopr/starting.html 
+2. Install the following packages in R:
+
+```{r installs, eval=FALSE}
+# Install basic data analysis packages
+rm(list = ls())
+install.packages("dplyr")     # Data manipulation
+install.packages("tidyr")     # Data tidying
+install.packages("ggplot2")   # Data visualization
+install.packages("readr")     # Data import
+install.packages("knitr")     # Document generation
+install.packages("data.table")# Efficient data handling
+install.packages("Matrix")    # Matrix operations
+
+# Install DemoKin
+# DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
+# but we'll use the development version on GitHub (https://github.com/IvanWilli/DemoKin):
+install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)  # For kinship analysis
+```
+
+# Setting Up the Analysis Environment {#load-packages}
+
+Let's begin by loading the necessary packages for our analysis:
+
+```{r libraries, warning=F, message=FALSE}
+library(dplyr)    # For data manipulation
+library(tidyr)    # For restructuring data
+library(ggplot2)  # For visualization
+library(readr)    # For reading data
+library(knitr)    # For document generation
+```
+
+<!-- Load additional utility functions that we've prepared for this tutorial: -->
+
+<!-- ```{r functions, warning=F, message=FALSE} -->
+<!-- source("functions.R") -->
+<!-- ``` -->
+
+# Understanding the Demographic Data {#demographic-data}
+
+## Data Overview
+
+The `DemoKin` package includes Swedish demographic data from the Human Mortality Database (HMD) and Human Fertility Database (HFD) as an example dataset. This includes:
+
+- **swe_px**: Age-by-year matrix of survival probabilities
+- **swe_Sx**: Age-by-year matrix of survival ratios
+- **swe_asfr**: Age-by-year matrix of fertility rates
+- **swe_pop**: Age-by-year matrix of population counts
+
+You can view all available data in the package with `data(package="DemoKin")`.
+
+## Exploring the Data
+
+Let's examine a subset of the Swedish demographic data to understand its structure:
+
+```{r data_exploration, warning=FALSE, message=FALSE}
+# First 5 rows and columns of survival probabilities
+head(swe_px[1:5, 1:5])
+
+# Fertility rates for ages 25-30
+head(swe_asfr[26:31, 1:10])
+```
+
+For our time-invariant model, we need to extract the demographic rates for a single year. Let's use 2015 as our reference year:
+
+```{r extract_2015, warning=FALSE, message=FALSE}
+# Extract vectors for 2015
+swe_surv_2015 <- swe_px[,"2015"]  # Survival probabilities
+swe_asfr_2015 <- swe_asfr[,"2015"] # Fertility rates
+```
+
+Let's compare the data between different time periods to understand demographic changes. Here we compare values from 1950 and 2010:
+
+```{r compare_periods, warning=FALSE, message=FALSE}
+# Survival probabilities
+cat("Survival probabilities (px):\n")
+head(swe_px[,c("1950","2010")])
+
+# Fertility rates
+cat("\nFertility rates (asfr):\n")
+head(swe_asfr[,c("1950","2010")])
+
+# Population counts
+cat("\nPopulation counts:\n")
+head(swe_pop[,c("1950","2010")])
+```
+
+## Visualizing Demographic Trends
+
+### Mortality Trends
+
+Let's visualize how mortality has changed over time. We'll plot the probability of dying between ages $x$ and $x+1$ (denoted as $q_x = 1-p_x$) for different years:
+
+```{r mortality_viz}
+swe_px %>%
+  as.data.frame() %>%
+  mutate(age = c(0:100)) %>%
+  pivot_longer(cols = -c(age), names_to = "year", values_to = "px") %>%
+  filter(year %in% seq(1950, 2010, 30)) %>% 
+  mutate(qx = 1-px) %>%
+  ggplot() +
+  geom_line(aes(x = age, y = qx, col = as.character(year)), linewidth = 1) +
+  scale_y_log10() +
+  labs(
+    title = "Age-specific mortality in Sweden (1950-2010)",
+    subtitle = "Probability of dying between ages x and x+1",
+    x = "Age",
+    y = "Probability of dying (qx, log scale)", 
+    col = "Year"
+  ) +
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
+
+**Interpretation**: This graph reveals how mortality has declined dramatically across all age groups from 1950 to 2010. The log scale highlights improvements at all ages, with particularly notable declines in infant and child mortality. The characteristic "bathtub" shape of human mortality is clearly visible: high mortality in infancy, followed by very low mortality through childhood and early adulthood, then a steady exponential increase with age.
+
+### Fertility Trends
+
+Now, let's examine how fertility patterns have changed over time:
+
+```{r fertility_viz}
+swe_asfr %>%
+  as.data.frame() %>%
+  mutate(age = c(0:100)) %>%
+  pivot_longer(cols = -c(age), names_to = "year", values_to = "fx") %>%
+  filter(year %in% seq(1950, 2010, 30)) %>% 
+  ggplot() + 
+  geom_line(aes(x = age, y = fx, col = as.character(year)), linewidth = 1) +
+  labs(
+    title = "Age-specific fertility in Sweden (1950-2010)",
+    subtitle = "Fertility rates by age of mother",
+    x = "Age of mother",
+    y = "Age-specific fertility rate (fx)", 
+    col = "Year"
+  ) +
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
+
+**Interpretation**: This visualization shows how fertility patterns have changed over the decades. The 1950 curve shows earlier childbearing with higher peak fertility rates. By 2010, fertility has shifted to later ages, reflecting the postponement of childbearing in developed countries. We can also observe the declining total fertility rate (the area under each curve).
+
+### Population Structure
+
+Finally, let's look at how the population structure has evolved:
+
+```{r population_viz}
+swe_pop %>%
+  as.data.frame() %>%
+  mutate(age = c(0:100)) %>%
+  pivot_longer(-age, names_to = "year", values_to = "pop") %>% 
+  mutate(year = gsub("X", "", year)) %>% 
+  filter(year %in% seq(1950, 2010, 30)) %>% 
+  ggplot() + 
+  geom_line(aes(x = age, y = pop, col = as.character(year)), linewidth = 1) +
+  labs(
+    title = "Female population structure in Sweden (1950-2010)",
+    subtitle = "Population counts by age",
+    x = "Age",
+    y = "Population count (thousands)", 
+    col = "Year"
+  ) +
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
+
+**Interpretation**: This graph shows how Sweden's female population structure has changed over time. The 1950 distribution shows the effects of baby booms and war years. By 2010, we see population aging with a more uniform distribution across ages and greater longevity, with significant numbers of women surviving to very old ages.
+
+# The DemoKin Package {#the-demokin-package}
+
+## Overview
+
+`DemoKin` is an R package designed to compute the number and age distribution of relatives (kin) of a focal individual under various demographic assumptions. It can analyze both living and deceased kin, and allows for both time-invariant and time-varying demographic rates.
+
+## The `kin()` Function {#kin-function}
+
+The main function in the package is `DemoKin::kin()`, which implements matrix kinship models to calculate expected kin counts.
+
+For our first example, we'll run the simplest model with the following assumptions:
+
+1. **Time-invariant** rates: The same set of mortality and fertility rates apply throughout all time periods (we'll use 2015 rates).
+2. **One-sex** population: We'll only use female data and trace kinship through female lines.
+
+Let's run the basic kinship model:
+
+```{r basic_kin_model}
+# Run the time-invariant, one-sex model
+swe_2015 <- kin(
+  p = swe_surv_2015,          # Vector of survival probabilities
+  f = swe_asfr_2015,          # Vector of fertility rates
+  time_invariant = TRUE       # Use time-invariant model
+)
+```
+
+## Function Arguments {#kin-arguments}
+
+The `kin()` function accepts several important arguments:
+
+- **p**: A vector or matrix of survival probabilities with rows as ages (and columns as years if a matrix)
+- **f**: A vector or matrix of fertility rates with the same dimensions as p
+- **time_invariant**: Logical flag indicating whether to assume time-invariant rates (default: TRUE)
+- **output_kin**: Character vector specifying which kin types to return (e.g., "m" for mother, "d" for daughter)
+
+## Relative Types {#relative-types}
+
+In `DemoKin`, each type of relative is identified by a unique code. These codes differ from those used in Caswell [-@caswell_formal_2019]. The following table shows the relationship between these coding systems:
+
+```{r relative_codes}
+# Display relationship codes
+demokin_codes
+```
+
+## Function Output {#value}
+
+The `kin()` function returns a list containing two data frames:
+
+```{r output_structure}
+# Examine the structure of the output
+str(swe_2015)
+```
+
+### The `kin_full` Data Frame {#kin-full}
+
+This data frame contains detailed information on expected kin counts by:
+- Age of the focal individual
+- Type of kin
+- Age of kin
+- Living/dead status
+
+```{r kin_full_example}
+# View the first few rows of kin_full
+head(swe_2015$kin_full)
+```
+
+### The `kin_summary` Data Frame {#kin-summary}
+
+This data frame provides a summary of expected kin counts by:
+- Age of the focal individual
+- Type of kin
+- Total counts (not broken down by age of kin)
+
+```{r kin_summary_example}
+# View the first few rows of kin_summary
+head(swe_2015$kin_summary)
+```
+
+# Visualizing Kinship Networks {#kinship-diagrams}
+
+## Keyfitz Diagrams
+
+One powerful way to visualize kinship structure is through a network or 'Keyfitz' kinship diagram [@Keyfitz2005]. Let's see the expected number of living female relatives for a 65-year-old woman according to our model:
+
+```{r keyfitz_diagram, fig.height=10, fig.width=12}
+swe_2015$kin_summary %>% 
+  filter(age_focal == 65) %>% 
+  select(kin, count = count_living) %>% 
+  plot_diagram(rounding = 2)
+```
+
+**Interpretation**: This Keyfitz diagram provides a comprehensive view of the kinship network for a 65-year-old woman in Sweden (based on 2015 demographic rates). The diagram shows:
+
+- Vertical relationships: A 65-year-old woman is likely to have around 0.9 daughters and 0.52 granddaughters through daughters, but few great-granddaughters (nearly 0) as they wouldn't have been born yet. Looking upward, she's unlikely to have a living mother (0.16) and almost certainly no living grandmother (nearly 0).
+- Horizontal relationships: She would have about 0.83 living sisters (0.38 old sisters and 0.45 younger sisters) and 0.8 nieces.
+
+This visualization helps us understand the changing composition of family networks across the life course.
+
+# Analyzing Living Kin Over the Life Course {#number-of-living-kin}
+
+Let's run the model again, but this time we'll specify exactly which kin types we want to analyze:
+
+```{r specific_kin_model}
+swe_2015 <- 
+  kin(
+    p = swe_surv_2015,
+    f = swe_asfr_2015,
+    output_kin = c("c", "d", "gd", "ggd", "gm", "m", "n", "a", "s"),  # Specific kin types
+    time_invariant = TRUE
+  )
+```
+
+Now, let's visualize how the expected number of each type of relative changes over the life course:
+
+```{r kin_over_lifecourse, fig.height=8, fig.width=10}
+swe_2015$kin_summary %>%
+  rename_kin() %>%  # Convert kin codes to readable labels
+  ggplot() +
+  geom_line(aes(age_focal, count_living), linewidth = 1)  +
+  theme_bw() +
+  labs(
+    title = "Expected number of living female relatives over the life course",
+    subtitle = "Based on Swedish demographic rates from 2015",
+    x = "Age of focal individual",
+    y = "Number of living female relatives"
+  ) +
+  facet_wrap(~kin_label, scales = "free_y")  # Use different y-scales for each panel
+```
+
+**Interpretation**: These plots show how different kinship relationships evolve over a person's lifetime:
+
+- **Mothers**: Initially 1.0 (everyone has a mother at birth), then gradually declining as mortality takes its toll
+- **Grandmothers**: Start lower (many already deceased at Focal's birth) and decline rapidly
+- **Daughters**: Increasing during reproductive years, then stable
+- **Granddaughters**: Appearing later and increasing as daughters have children
+- **Great-granddaughters**: Appearing even later as granddaughters have children
+- **Sisters**: Relatively stable then declining due to mortality
+- **Aunts and cousins**: Follow similar patterns of eventual decline
+- **nieces**: similar patterns as daughters.
+
+> Note that we are working in a time-invariant framework. You can think of the results as analogous to life expectancy (i.e., expected years of life for a synthetic cohort experiencing a given set of period mortality rates).
+
+## Total Family Size Over the Life Course
+
+How does the overall family size (and family composition) vary over life for an average woman?
+
+```{r family_size_composition}
+# Calculate total kin count at each age
+counts <- 
+  swe_2015$kin_summary %>%
+  group_by(age_focal) %>% 
+  summarise(count_living = sum(count_living)) %>% 
+  ungroup()
+
+# Plot family composition over the life course
+swe_2015$kin_summary %>%
+  select(age_focal, kin, count_living) %>% 
+  rename_kin() %>% 
+  ggplot(aes(x = age_focal, y = count_living)) +
+  geom_area(aes(fill = kin_label), color = "black", alpha = 0.8) +
+  geom_line(data = counts, linewidth = 1.5) +
+  labs(
+    title = "Family size and composition over the life course",
+    subtitle = "Based on Swedish demographic rates from 2015",
+    x = "Age of focal individual",
+    y = "Number of living female relatives",
+    fill = "Kin type"
+  ) +
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
+
+**Interpretation**: This stacked area chart reveals fascinating patterns in family size and composition throughout life:
+
+1. **Early life**: Family consists primarily of mothers, grandmothers, aunts, cousins, and sisters
+2. **Young and middle adulthood (20s-40s)**: Total family size increases as daughters and nieces are born
+3. **Late adulthood and Older (50s+)**: Even though granddaughters and granddaughters are born, while older relatives (mothers, aunts, grandmothers) begin to disappear. Family composition shifts dramatically toward descendants (daughters, granddaughters, great-granddaughters)
+
+Therefore, the total family size (black line) shows an interesting U-shape, first declining as older relatives die, then rising again as new generations are born.
+
+# Age Distribution of Relatives {#age-distribution-of-living-kin}
+
+Beyond just counting relatives, we're often interested in their age distribution. Using the `kin_full` data frame, we can examine the age distribution of Focal's relatives at a specific age.
+
+Let's visualize the age distribution of relatives when Focal is 65 years old:
+
+```{r age_distribution, fig.height=8, fig.width=10}
+swe_2015$kin_full %>%
+  rename_kin() %>%
+  filter(age_focal == 65) %>%
+  ggplot(aes(age_kin, living)) +
+  geom_line(linewidth = 1) +
+  geom_vline(xintercept = 65, color = "red", linetype = "dashed") +
+  labs(
+    title = "Age distribution of living female relatives when Focal is 65",
+    subtitle = "Based on Swedish demographic rates from 2015 (red line = Focal's age)",
+    x = "Age of relative",
+    y = "Expected number of living relatives"
+  ) +
+  theme_bw() +
+  facet_wrap(~kin_label, scales = "free_y")
+```
+
+**Interpretation**: These distributions provide rich information about family age structure:
+
+- **Mothers**: If still alive, would be concentrated around age 85-95
+- **Daughters**: Mostly in their 30s and 40s
+- **Granddaughters**: Predominantly young, between ages 0-15
+- **Sisters**: Close to Focal's own age (65)
+- **Nieces**: Mostly in their 30s and 40s, similar to daughters
+- **Cousins**: Close to Focal's own age (65)
+
+Understanding age distributions is crucial for estimating care needs, support systems, and intergenerational transfers within families.
+
+# Conclusion
+
+In this tutorial, we've explored how to use the `DemoKin` package to model kinship dynamics in a time-invariant, one-sex framework. We've seen how different demographic patterns affect family size and composition, and visualized these relationships across the life course.
+
+Key insights include:
+
+1. Family networks are dynamic, changing dramatically throughout the life course
+2. Both family size and composition evolve with age
+3. Modern demographic rates lead to "bean pole" families—vertical extension (multiple generations) but horizontal contraction (fewer siblings, cousins)
+4. Matrix population models provide a powerful framework for understanding these dynamics
+
+# References
diff --git a/vignettes/1_2_OneSex_TimeVarying_Age.Rmd b/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
new file mode 100644
index 0000000..f82a260
--- /dev/null
+++ b/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
@@ -0,0 +1,319 @@
+---
+title: "One-sex time-varying kinship model specified by age"
+output:
+  html_document:
+    toc: true
+    toc_float: 
+      collapsed: false
+      smooth_scroll: true
+    theme: readable
+    highlight: pygments
+    number_sections: true
+    code_folding: show
+    df_print: paged
+    fig_caption: true
+bibliography: references.bib
+vignette: >
+  %\VignetteIndexEntry{One-sex time-varying kinship model specified by age}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include=FALSE}
+# Set up code chunk options
+knitr::opts_chunk$set(echo = TRUE,
+                      message = FALSE, 
+                      warning = FALSE, 
+                      fig.align = 'center',
+                      fig.width = 8,
+                      fig.height = 6,
+                      dpi = 300)
+# Prevent scientific notation (useful for the rate calculation)
+options(scipen = 999999)
+pkgload::load_all()
+```
+
+<style>
+/* Make TOC appear on the right side */
+.tocify {
+  position: fixed;
+  top: 50px;
+  right: 0;
+  width: 240px;
+  height: calc(100% - 50px);
+  overflow-y: auto;
+  margin-left: 0;
+  padding-left: 0;
+}
+
+/* Adjust main content to make room for right-side TOC */
+.main-container {
+  max-width: calc(100% - 260px);
+  margin-left: 10px;
+  margin-right: 250px;
+}
+
+/* Limit TOC to only show 2 levels */
+.tocify-subheader .tocify-subheader {
+  display: none;
+}
+
+/* Style for learning objectives box */
+.learning-objectives {
+  background-color: #e0f7fa;
+  border-left: 5px solid #00acc1;
+  padding: 15px;
+  margin-bottom: 20px;
+  border-radius: 3px;
+}
+</style>
+
+<div class="learning-objectives">
+<strong>Learning Objectives</strong>: In this vignette, you will learn how to extend the DemoKin time-invariant model to incorporate changing demographic rates over time. You will understand how to implement a time-varying kinship model, examine the impact of demographic change on kinship networks, and analyze related phenomena such as kin loss and prevalence of specific conditions among kin.
+</div>
+
+# Introduction {#introduction}
+
+While the time-invariant model we explored in the previous vignette provides valuable insights into kinship structures, it has one significant limitation: it assumes demographic conditions remain constant throughout a person's life. In reality, mortality and fertility rates evolve dramatically over time due to socioeconomic development, medical advances, and cultural shifts.
+
+Time-varying kinship models address this limitation by incorporating historical demographic changes, offering a more nuanced and realistic picture of family networks. These advanced models allow us to:
+
+- Track kinship networks for specific birth cohorts across their life course
+- Account for the different demographic conditions experienced by each generation
+- Provide more accurate estimates of kin availability in specific historical periods
+- Better understand how demographic transitions shape family structures
+
+In this vignette, we will implement a **one-sex time-varying model**, outlined in Caswell and Song [-@caswell_formal_2021], using the `DemoKin` package. We build on the time-invariant approach but incorporate year-specific mortality and fertility rates to model the kinship networks of individuals born in specific years.
+
+## Package Installation {#preparation}
+
+If you haven't already installed the required packages from the previous vignette, here's what you'll need:
+
+```{r installs, eval=FALSE}
+# Install basic data analysis packages
+rm(list = ls())
+install.packages("dplyr")     # Data manipulation
+install.packages("tidyr")     # Data tidying
+install.packages("ggplot2")   # Data visualization
+install.packages("readr")     # Data import
+install.packages("knitr")     # Document generation
+install.packages("data.table")# Efficient data handling
+install.packages("Matrix")    # Matrix operations
+
+# Install DemoKin
+# DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
+# but we'll use the development version on GitHub (https://github.com/IvanWilli/DemoKin):
+install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)  # For kinship analysis
+```
+
+# Setting Up the Analysis Environment {#load-packages}
+
+Let's load the necessary packages for our analysis:
+
+```{r libraries, warning=F, message=FALSE}
+library(dplyr)    # For data manipulation
+library(tidyr)    # For restructuring data
+library(ggplot2)  # For visualization
+library(readr)    # For reading data
+library(knitr)    # For document generation
+```
+
+<!-- Load the utility functions we've prepared: -->
+
+<!-- ```{r functions, warning=F, message=FALSE} -->
+<!-- source("functions.R") -->
+<!-- ``` -->
+
+# Time-Varying Kinship Models {#time-varying-models}
+
+## The Conceptual Shift
+
+In the time-invariant model, we assumed that everyone experiences the same mortality and fertility rates throughout their lives (e.g., the 2015 rates). However, this is a simplification of reality. Demographic rates change over time, often dramatically:
+
+- A woman born in 1900 would have experienced very different mortality risks at age 20 (in 1920) than a woman born in 1950 would have at age 20 (in 1970)
+- Similarly, fertility patterns have shifted substantially over generations
+
+Time-varying models account for these historical changes by using year-specific demographic rates. They provide a more realistic picture of kinship dynamics for specific birth cohorts as they age through changing demographic conditions.
+
+## Data Requirements
+
+For time-varying models, instead of vectors of rates for a single year, we need:
+
+1. A matrix of survival probabilities, with:
+   - Rows representing ages (0, 1, 2, ..., 100+)
+   - Columns representing years (e.g., 1950, 1951, ..., 2020)
+
+2. A matrix of fertility rates with the same dimensions
+
+3. A matrix of population counts (optional, for certain calculations)
+
+The `DemoKin` package includes Swedish data in this format, which we'll use for our example.
+
+## Implementing the Time-Varying Model {#run-the-model-time-varying}
+
+Let's implement a time-varying kinship model for women born in 1960 in Sweden. We'll focus on specific kin types to make interpretation easier:
+
+```{r}
+swe_time_varying <- 
+  kin(
+    p = swe_px,              # Matrix of survival probabilities by age and year
+    f = swe_asfr,            # Matrix of fertility rates by age and year
+    n = swe_pop,             # Matrix of population counts by age and year
+    time_invariant = FALSE,  # Use time-varying model
+    output_cohort = 1960,    # Focus on the 1960 birth cohort
+    output_kin = c("d","gd","ggd","m","gm","ggm")  # Select specific kin types
+    )
+```
+
+In this model:
+- We use the full matrices of Swedish demographic data (`swe_px`, `swe_asfr`, `swe_pop`)
+- We set `time_invariant = FALSE` to implement a time-varying model
+- We specify `output_cohort = 1960` to focus on women born in 1960
+- We select specific relatives to analyze (daughters, granddaughters, great-granddaughters, mothers, grandmothers, and great-grandmothers)
+
+## Living Relatives Across the Life Course {#living-relatives}
+
+Let's examine how the number of living kin changes throughout the life course for the 1960 birth cohort:
+
+```{r, fig.height=6, fig.width=8}
+swe_time_varying$kin_summary %>%
+  ggplot(aes(age_focal, count_living, color=factor(cohort))) +
+  scale_y_continuous(name = "", labels = seq(0,3,.2), breaks = seq(0,3,.2)) +
+  geom_line(color = 1) +
+  geom_vline(xintercept = 35, color=2) +
+  labs(
+    title = "Expected number of living relatives for the 1960 birth cohort",
+    subtitle = "Swedish demographic rates, time-varying model",
+    x = "Age of focal individual",
+    y = "Expected number of living relatives"
+  ) +
+  facet_wrap(~kin, scales = "free") +
+  theme_bw()
+```
+
+**Interpretation**: These plots show how the expected number of living relatives changes as the 1960 cohort ages:
+
+- **Mothers (m)**: Starts near 1.0 and gradually declines as mothers die
+- **Grandmothers (gm)**: Already well below 1.0 at birth, reflecting pre-1960 mortality, then declining rapidly
+- **Great-grandmothers (ggm)**: Very few at birth, quickly disappearing
+- **Daughters (d)**: Increasing during reproductive years, reflecting fertility patterns of the 1980s-2000s
+- **Granddaughters (gd)**: Appearing as daughters reach reproductive age, reflecting fertility of the 2000s-2030s
+- **Great-granddaughters (ggd)**: Beginning to appear in later years
+
+The red vertical line at age 35 provides a reference point to compare kin counts at a specific age. Unlike the time-invariant model, these counts reflect the actual historical demographic conditions experienced by this cohort and their relatives.
+
+## Analyzing Kin Loss {#kin-loss}
+
+Beyond counting living kin, we can also examine patterns of kin mortality. Understanding kin loss is important because it:
+
+- Has psychological and social consequences for bereaved individuals
+- Affects the availability of support and care across generations
+- Influences patterns of inheritance and resource transfers
+
+Let's examine the cumulative number of deceased relatives by age for our 1960 birth cohort:
+
+```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE}
+swe_time_varying$kin_summary %>%
+  ggplot() +
+  geom_line(aes(age_focal, count_cum_dead)) +
+  labs(
+    title = "Cumulative number of deceased relatives by age",
+    subtitle = "1960 birth cohort, Sweden",
+    x = "Age of focal individual",
+    y = "Expected number of deceased relatives"
+  ) +
+  theme_bw() +
+  facet_wrap(~kin, scales="free")
+```
+
+**Interpretation**: These graphs show the cumulative number of deaths experienced by kin type:
+
+- **Ascending relatives**: Deaths accumulate gradually for mothers, and more rapidly for grandmothers and great-grandmothers
+- **Descending relatives**: Deaths are rare but do occur, representing the tragedy of losing children, grandchildren, or great-grandchildren
+
+We can also examine the mean age at which relatives die. For a 50-year-old woman born in 1960:
+
+```{r}
+swe_time_varying$kin_summary %>% 
+  filter(age_focal == 50) %>% 
+  select(kin, count_cum_dead, mean_age_lost) %>% 
+  mutate_if(is.numeric, round, 2) %>% 
+  kable()
+```
+
+This table shows both the expected number of deceased relatives and the mean age at which they died. For example, by age 50, a woman born in 1960 would have lost approximately 0.32 mothers and the age at which such women lost mother is around 37.83 years-old on average.
+
+## Prevalence Calculations {#prevelances}
+
+Beyond simple counts, we can combine kinship data with prevalence rates by age to estimate the number of kin with specific characteristics. This approach, based on the Sullivan Method, allows us to:
+
+- Estimate relatives with specific health conditions
+- Calculate working-age vs. dependent kin
+- Project care needs or support capacity within family networks
+
+Let's create a hypothetical prevalence vector that increases exponentially with age (which might represent a condition like dementia):
+
+```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
+# Create a prevalence vector that increases exponentially with age
+swe_prevalence <- 
+  tibble(
+    age_kin = unique(swe_time_varying$kin_full$age_kin),
+    prev = .005 * exp(.05 * age_kin)  # Exponential increase with age
+    )
+
+# Combine with kinship data and calculate counts
+swe_time_varying$kin_full %>% 
+  left_join(swe_prevalence) %>% 
+  group_by(kin, age_focal, cohort) %>% 
+  summarise(
+    prevalent = sum(living * prev),        # Kin with the condition
+    no_prevalent = sum(living * (1-prev))  # Kin without the condition
+    ) %>% 
+  pivot_longer(cols = prevalent:no_prevalent, names_to = "prevalence_state", values_to = "count") %>% 
+  ggplot(aes(x=age_focal, y = count)) + 
+  geom_area(aes(fill=prevalence_state)) +
+  labs(
+    title = "Expected number of relatives with and without the condition",
+    subtitle = "Based on age-specific prevalence rates",
+    x = "Age of focal individual",
+    y = "Number of living relatives",
+    fill = "Condition status"
+  ) +
+  facet_wrap(~kin) +
+  theme_bw()
+```
+
+**Interpretation**: The stacked area plots show the expected number of relatives with and without the hypothetical condition at each age of Focal:
+
+- For older relatives (mothers, grandmothers), the proportion with the condition increases as Focal ages, reflecting the age-related nature of the condition
+- For younger relatives (daughters, granddaughters), the prevalence remains low, consistent with the age pattern of the condition
+- We can see both the changing total number of relatives and the changing composition by condition status
+
+This approach can be extended to any age-specific prevalence, such as:
+
+- Health conditions (disability, chronic disease)
+- Employment status
+- Educational attainment
+- Living arrangements
+
+# Conclusion
+
+In this vignette, we've explored how to implement time-varying kinship models using the `DemoKin` package, expanding our analytical approach to incorporate historical demographic change.
+
+The time-varying approach offers several advantages over time-invariant models:
+1. **Historical accuracy**: It incorporates actual demographic changes rather than assuming constant rates
+2. **Cohort specificity**: It can model specific birth cohorts experiencing their unique demographic conditions
+3. **Period effects**: It captures major demographic events like wars, pandemics, or baby booms
+
+These methodological improvements allow us to uncover important substantive insights:
+
+- How demographic transitions reshape family structures across generations
+- The unique kinship experiences of different birth cohorts as they navigate through changing mortality and fertility regimes
+- How kin loss and bereavement patterns evolve in response to mortality improvements
+- The complex interplay between period and cohort effects in shaping family networks
+
+While time-varying models provide greater realism, time-invariant models still serve valuable purposes. They offer a simpler baseline for understanding kinship patterns, can project future kinship structures based on current demographic conditions, and require less data.
+
+# References
diff --git a/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd b/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
new file mode 100644
index 0000000..c8211ff
--- /dev/null
+++ b/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
@@ -0,0 +1,360 @@
+---
+title: "Two-sex time-invariant kinship model specified by age"
+output:
+  html_document:
+    toc: true
+    toc_float: 
+      collapsed: false
+      smooth_scroll: true
+    theme: readable
+    highlight: pygments
+    number_sections: true
+    code_folding: show
+    df_print: paged
+    fig_caption: true
+bibliography: references.bib
+vignette: >
+  %\VignetteIndexEntry{Two-sex time-invariant kinship model specified by age}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include=FALSE}
+# Set up code chunk options
+knitr::opts_chunk$set(echo = TRUE,
+                      message = FALSE, 
+                      warning = FALSE, 
+                      fig.align = 'center',
+                      fig.width = 8,
+                      fig.height = 6,
+                      dpi = 300)
+# Prevent scientific notation (useful for the rate calculation)
+options(scipen = 999999)
+pkgload::load_all()
+```
+
+<style>
+/* Make TOC appear on the right side */
+.tocify {
+  position: fixed;
+  top: 50px;
+  right: 0;
+  width: 240px;
+  height: calc(100% - 50px);
+  overflow-y: auto;
+  margin-left: 0;
+  padding-left: 0;
+}
+
+/* Adjust main content to make room for right-side TOC */
+.main-container {
+  max-width: calc(100% - 260px);
+  margin-left: 10px;
+  margin-right: 250px;
+}
+
+/* Limit TOC to only show 2 levels */
+.tocify-subheader .tocify-subheader {
+  display: none;
+}
+
+/* Style for learning objectives box */
+.learning-objectives {
+  background-color: #e0f7fa;
+  border-left: 5px solid #00acc1;
+  padding: 15px;
+  margin-bottom: 20px;
+  border-radius: 3px;
+}
+</style>
+
+<div class="learning-objectives">
+<strong>Learning Objectives</strong>: In this vignette, you will learn how to extend the one-sex kinship model to incorporate both male and female demographic rates. You will understand the implementation of two-sex matrix models, explore how sex-specific mortality and fertility patterns affect kinship structures, and analyze differences in kin availability by sex.
+</div>
+
+# Introduction {#introduction}
+
+Demographic processes fundamental to kinship formation vary significantly between males and females. While one-sex models offer valuable insights into family structures, they overlook these sex differences, which can lead to incomplete understanding of kinship dynamics. Two-sex kinship models address this limitation by incorporating sex-specific demographic rates and tracing both male and female lineages.
+
+Key advantages of two-sex models include:
+
+- Accounting for sex differences in mortality
+- Incorporating sex-specific fertility patterns
+- Enabling analysis of kin availability by sex
+- Allowing for the exploration of sex ratios within kinship networks
+- Providing more realistic estimates of kin availability across the life course
+
+In this vignette, we will implement a **two-sex time-invariant kinship model**, outlined in Caswell [-@caswell_formal_2022], using the `DemoKin` package to understand how sex-specific demographic patterns shape family structures.
+
+## Package Installation {#preparation}
+
+If you haven't already installed the required packages from the previous vignettes, here's what you'll need:
+
+```{r installs, eval=FALSE}
+# Install basic data analysis packages
+install.packages("dplyr")     # Data manipulation
+install.packages("tidyr")     # Data tidying
+install.packages("ggplot2")   # Data visualization
+install.packages("readr")     # Data import
+install.packages("knitr")     # Document generation
+install.packages("data.table")# Efficient data handling
+install.packages("Matrix")    # Matrix operations
+
+# Install DemoKin
+# DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
+# but we'll use the development version on GitHub (https://github.com/IvanWilli/DemoKin):
+install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)  # For kinship analysis
+```
+
+# Setting Up the Analysis Environment {#load-packages}
+
+Let's load the necessary packages for our analysis:
+
+```{r libraries, warning=F, message=FALSE}
+rm(list = ls())
+library(dplyr)    # For data manipulation
+library(tidyr)    # For restructuring data
+library(ggplot2)  # For visualization
+library(readr)    # For reading data
+library(knitr)    # For document generation
+```
+
+# Two-Sex Kinship Modeling {#two-sex-model}
+
+## Understanding Sex Differences in Demographic Rates {#model-input-2sex}
+
+The first step in implementing a two-sex kinship model is to understand the sex differences in demographic rates. Human males and females exhibit distinct mortality and fertility patterns:
+
+1. **Mortality differences**: Males generally experience higher mortality rates at all ages, resulting in shorter life expectancy
+2. **Fertility differences**: Males often begin reproduction later and can continue reproducing at older ages
+
+These differences affect kinship structures in several important ways:
+
+- The availability of male versus female relatives (especially at older ages)
+- The timing of kin loss experiences (e.g., when fathers versus mothers die)
+- The number of descendants for male versus female individuals
+
+For our example, we'll use data from France (2012), which is included in the `DemoKin` package. Let's examine the sex-specific mortality and fertility rates:
+
+```{r sex_differences, fig.height= 8, fig.width= 10}
+# Extract sex-specific rates
+fra_fert_f <- fra_asfr_sex[,"ff"]    # Female fertility rates
+fra_fert_m <- fra_asfr_sex[,"fm"]    # Male fertility rates
+fra_surv_f <- fra_surv_sex[,"pf"]    # Female survival probabilities
+fra_surv_m <- fra_surv_sex[,"pm"]    # Male survival probabilities
+
+# Compare total fertility rates by sex
+cat("Difference in TFR (male - female):", sum(fra_fert_m) - sum(fra_fert_f))
+
+# Visualize sex differences in demographic rates
+data.frame(value = c(fra_fert_f, fra_fert_m, fra_surv_f, fra_surv_m),
+           age = rep(0:100, 4),
+           sex = rep(c(rep("f", 101), rep("m", 101)), 2),
+           risk = c(rep("fertility rate", 101 * 2), rep("survival probability", 101 * 2))) %>%
+  ggplot(aes(age, value, col=sex)) + 
+  geom_line(linewidth = 1) + 
+  labs(
+    title = "Sex-specific demographic rates in France (2012)",
+    x = "Age",
+    y = "Rate",
+    color = "Sex"
+  ) +
+  facet_wrap(~ risk, scales = "free_y") + 
+  theme_bw()
+```
+
+**Interpretation**: The graphs reveal important sex differences in demographic rates:
+
+- **Fertility patterns**: While total fertility rates are nearly identical between males and females (difference of only 0.01), the age patterns differ substantially. Male fertility occurs at later ages and has a wider distribution, reflecting the tendency for men to father children at older ages compared to women.
+
+- **Survival probabilities**: Females have higher survival probabilities at most of adult and old ages. This pattern leads to sex imbalances in older populations and affects the availability of different types of relatives.
+
+These sex differences in demographic rates will shape kinship networks in ways that one-sex models cannot capture.
+
+## Implementing the Two-Sex Model {#run-model-2sex}
+
+We now introduce the function `kin2sex`, which extends the one-sex function `kin` to incorporate sex-specific rates. The key differences are:
+
+1. We need to provide both female and male demographic rates
+2. We must specify the sex of the focal individual
+3. We need to indicate the sex ratio at birth (proportion of births that are female)
+
+Let's implement a two-sex time-varying model for France:
+
+```{r two_sex_model}
+kin_result <- kin2sex(
+  pf = fra_surv_f,        # Female survival probabilities
+  pm = fra_surv_m,        # Male survival probabilities
+  ff = fra_fert_f,        # Female fertility rates
+  fm = fra_fert_m,        # Male fertility rates
+  time_invariant = TRUE,  # Use time-invariant model
+  sex_focal = "f",        # Focus on female focal individuals
+  birth_female = .5       # Proportion of births that are female
+)
+```
+
+The output of `kin2sex` is similar to that of `kin`, with an additional column `sex_kin` that specifies the sex of each relative.
+
+## Living Relatives by Sex {#living-relatives-by-sex}
+
+Let's examine how the number of living relatives differs by sex across the life course of a female focal individual:
+
+```{r living_by_sex, message=FALSE, warning=FALSE}
+# Group specific kin types and filter for key relationships
+kin_out <- kin_result$kin_summary %>% 
+  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",      # Siblings
+                         kin %in% c("ya", "oa") ~ "a",    # Aunts/uncles
+                         TRUE ~ kin)) %>%
+  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a"))     # Select key relationships
+
+# Visualize living kin by sex
+kin_out %>% 
+  group_by(kin, age_focal, sex_kin) %>%
+  summarise(count = sum(count_living)) %>%
+  ggplot(aes(age_focal, count, fill = sex_kin)) +
+  geom_area() +
+  labs(
+    title = "Expected number of living relatives by sex",
+    subtitle = "Female focal individual, France 2012",
+    x = "Age of focal individual",
+    y = "Number of living relatives",
+    fill = "Sex of relative"
+  ) +
+  theme_bw() +
+  facet_wrap(~kin, labeller = labeller(
+    kin = c("a" = "Aunts/Uncles", "d" = "Children", 
+            "gm" = "Grandparents", "ggm" = "Great-grandparents",
+            "m" = "Parents", "s" = "Siblings")
+  ))
+```
+
+**Interpretation**: These stacked area plots reveal how the sex composition of living relatives changes across the life course:
+
+- **Parents (m)**: Fathers (blue) die earlier than mothers (red), leading to a predominance of mothers at older ages
+- **Grandparents (gm)**: Even at birth, grandmothers outnumber grandfathers due to mortality in the grandparental generation
+- **Great-grandparents (ggm)**: Shows an even stronger female predominance due to compounded mortality differences across generations
+- **Siblings (s)**: Brothers die earlier than sisters, leading to a higher proportion of sisters at older ages
+- **Children (d)**: Starts with an even sex ratio, with slight female predominance at older ages due to higher male mortality
+
+These patterns highlight the importance of accounting for sex differences in kinship models, especially when studying older populations.
+
+## Understanding Kinship Terminology in Two-Sex Models {#kinship-terminology}
+
+When using the `kin2sex` function, it's important to understand how relationship codes work:
+
+```{r terminology_note}
+# Example of how to identify specific relatives by sex
+kin_result$kin_summary %>% 
+  filter(kin == "d", sex_kin == "m") %>%  # This selects sons (male children)
+  head()
+```
+
+The function uses the same relationship codes as the one-sex model (see `demokin_codes()`), but now each relative has a specified sex. For example:
+
+- `kin = "d", sex_kin = "f"` refers to daughters
+- `kin = "d", sex_kin = "m"` refers to sons
+- `kin = "m", sex_kin = "f"` refers to mothers
+- `kin = "m", sex_kin = "m"` refers to fathers
+
+This coding system allows for flexible analysis of specific relative types while maintaining compatibility with the one-sex model.
+
+## Sex Ratios in Kinship Networks {#sex-ratios}
+
+Sex ratios (males per female) are a traditional measure in demography that can provide insights into kinship structures. Let's examine how sex ratios vary across different types of relatives:
+
+```{r sex_ratios, message=FALSE, warning=FALSE}
+# Calculate sex ratios (males per female) by kin type and age
+kin_out %>% 
+  group_by(kin, age_focal) %>%
+  summarise(sex_ratio = sum(count_living[sex_kin == "m"], na.rm = TRUE) / 
+                        sum(count_living[sex_kin == "f"], na.rm = TRUE)) %>%
+  ggplot(aes(age_focal, sex_ratio)) +
+  geom_line(linewidth = 1) +
+  geom_hline(yintercept = 1, linetype = "dashed", color = "gray50") +
+  labs(
+    title = "Sex ratios of living relatives across the life course",
+    subtitle = "Males per female, France 2012",
+    x = "Age of focal individual",
+    y = "Sex ratio (m/f)"
+  ) +
+  theme_bw() +
+  facet_wrap(~kin, scales = "free", labeller = labeller(
+    kin = c("a" = "Aunts/Uncles", "d" = "Children", 
+            "gm" = "Grandparents", "ggm" = "Great-grandparents",
+            "m" = "Parents", "s" = "Siblings")
+  ))
+```
+
+**Interpretation**: The sex ratio plots reveal several important patterns:
+
+- **Parents**: The sex ratio starts at 1 (equal numbers of mothers and fathers) but declines rapidly with age, reflecting higher male mortality
+- **Grandparents**: Even at birth, the sex ratio is below 1, with a 25-year-old having only about 0.5 grandfathers per grandmother
+- **Great-grandparents**: Shows even more extreme female predominance
+- **Children**: Maintains a sex ratio close to 1 throughout life, with slight declines at older ages
+- **Siblings**: Shows gradual decline in sex ratio with age due to higher male mortality
+
+These sex ratios have important implications for care relationships and support networks, particularly in older populations where female relatives predominate.
+
+## Timing of Kin Loss by Sex {#kin-loss-by-sex}
+
+The experience of losing relatives differs by the sex of those relatives. Let's examine how the timing of kin loss varies by sex:
+
+```{r kin_loss, message=FALSE, warning=FALSE}
+# Visualize dead kin by sex
+kin_out %>%
+  group_by(kin, sex_kin, age_focal) %>%
+  summarise(count = sum(count_dead)) %>%
+  ggplot(aes(age_focal, count, color = sex_kin)) +
+  geom_line(linewidth = 1) +
+  labs(
+    title = "Number of deceased relatives by sex",
+    subtitle = "Female focal individual, France 2012",
+    x = "Age of focal individual",
+    y = "Number of deceased relatives",
+    color = "Sex of relative"
+  ) +
+  theme_bw() +
+  facet_wrap(~kin, scales = "free", labeller = labeller(
+    kin = c("a" = "Aunts/Uncles", "d" = "Children", 
+            "gm" = "Grandparents", "ggm" = "Great-grandparents",
+            "m" = "Parents", "s" = "Siblings")
+  ))
+```
+
+**Interpretation**: These curves show how the experience of losing relatives differs by sex:
+
+- **Parents**: The loss of fathers (blue) occurs earlier than the loss of mothers (red)
+- **Grandparents**: Grandfather are often lost before birth or early in life, while grandmothers tend to be lost later
+- **Siblings**: Brothers are lost at higher rates than sisters before old ages (75+)
+- **Children**: While rare, the loss of sons occurs at higher rates than daughters
+
+Understanding these patterns is important for studying bereavement experiences and their impacts across the life course.
+
+# Applications of Two-Sex Kinship Models
+
+Two-sex kinship models have numerous applications in demographic and social research:
+
+1. **Gender and care**: Women typically provide more informal care to relatives than men. Two-sex models can help quantify potential care burdens by examining the availability of different types of relatives by sex.
+
+2. **Kinship networks in aging societies**: As populations age, the sex composition of available kin changes dramatically. Two-sex models allow us to project these changes and their implications for social support.
+
+3. **Intergenerational transfers**: Resources often flow differently between male and female relatives. Two-sex models provide the demographic foundation for studying these gendered patterns.
+
+4. **Demographic transitions**: Sex differences in mortality and fertility change during demographic transitions, reshaping kinship networks in ways that one-sex models cannot capture.
+
+5. **Demographic shocks**: Events like wars often affect males and females differently, with long-lasting impacts on kinship structures. Two-sex models can capture these effects.
+
+# Conclusion
+
+In this vignette, we've explored how to implement two-sex kinship models using the `DemoKin` package. By incorporating sex-specific mortality and fertility rates, these models reveal important patterns that one-sex models cannot capture:
+
+1. Female predominance among older relatives due to sex differences in mortality
+2. Systematic differences in the timing of kin loss by sex, with male kin typically lost earlier
+3. Varying sex ratios within kinship networks by relationship type and age
+4. Distinct age distributions of relatives by sex
+
+These insights have significant implications for understanding care relationships, intergenerational transfers, and support systems in aging societies. The two-sex approach substantially enhances our understanding of how gender shapes family structures across the life course, providing a more realistic foundation for both research and policy development.
+
+# References
diff --git a/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd b/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
new file mode 100644
index 0000000..90452ea
--- /dev/null
+++ b/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
@@ -0,0 +1,435 @@
+---
+title: "Two-sex time-varying kinship model specified by age"
+output:
+  html_document:
+    toc: true
+    toc_float: 
+      collapsed: false
+      smooth_scroll: true
+    theme: readable
+    highlight: pygments
+    number_sections: true
+    code_folding: show
+    df_print: paged
+    fig_caption: true
+bibliography: references.bib
+vignette: >
+  %\VignetteIndexEntry{Two-sex time-varying kinship model specified by age}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include=FALSE}
+# Set up code chunk options
+knitr::opts_chunk$set(echo = TRUE,
+                      message = FALSE, 
+                      warning = FALSE, 
+                      fig.align = 'center',
+                      fig.width = 8,
+                      fig.height = 6,
+                      dpi = 300)
+# Prevent scientific notation (useful for the rate calculation)
+options(scipen = 999999)
+pkgload::load_all()
+```
+
+<style>
+/* Make TOC appear on the right side */
+.tocify {
+  position: fixed;
+  top: 50px;
+  right: 0;
+  width: 240px;
+  height: calc(100% - 50px);
+  overflow-y: auto;
+  margin-left: 0;
+  padding-left: 0;
+}
+
+/* Adjust main content to make room for right-side TOC */
+.main-container {
+  max-width: calc(100% - 260px);
+  margin-left: 10px;
+  margin-right: 250px;
+}
+
+/* Limit TOC to only show 2 levels */
+.tocify-subheader .tocify-subheader {
+  display: none;
+}
+
+/* Style for learning objectives box */
+.learning-objectives {
+  background-color: #e0f7fa;
+  border-left: 5px solid #00acc1;
+  padding: 15px;
+  margin-bottom: 20px;
+  border-radius: 3px;
+}
+</style>
+
+<div class="learning-objectives">
+<strong>Learning Objectives</strong>: In this vignette, you will learn how to implement a kinship model that combines both two-sex and time-varying approaches. You will understand how to incorporate sex-specific demographic rates that change over time, analyze the effects of demographic transition on kinship structures by sex, and explore approximation methods when data is limited.
+</div>
+
+# Introduction {#introduction}
+
+Family networks are shaped simultaneously by two fundamental forces: demographic differences between males and females, and historical changes in demographic rates over time. While previous vignettes have explored these dimensions separately, this vignette integrates them into a more comprehensive framework: a **two-sex time-varying kinship model**, outlined in Caswell [-@caswell_formal_2022], using the `kin2sex` function with `time_invariant = FALSE`.
+
+## Package Installation {#preparation}
+
+If you haven't already installed the required packages from the previous vignettes, here's what you'll need:
+
+```{r installs, eval=FALSE}
+# Install basic data analysis packages
+install.packages("dplyr")     # Data manipulation
+install.packages("tidyr")     # Data tidying
+install.packages("ggplot2")   # Data visualization
+install.packages("readr")     # Data import
+install.packages("knitr")     # Document generation
+install.packages("data.table")# Efficient data handling
+install.packages("Matrix")    # Matrix operations
+
+# Install DemoKin
+# DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
+# but we'll use the development version on GitHub (https://github.com/IvanWilli/DemoKin):
+install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)  # For kinship analysis
+```
+
+# Setting Up the Analysis Environment {#load-packages}
+
+Let's load the necessary packages for our analysis:
+
+```{r libraries, warning=F, message=FALSE}
+rm(list = ls())
+library(dplyr)    # For data manipulation
+library(tidyr)    # For restructuring data
+library(ggplot2)  # For visualization
+library(readr)    # For reading data
+library(knitr)    # For document generation
+```
+
+# Two-Sex Time-Varying Kinship Models {#model-overview}
+
+## Model Structure and Components
+
+The combined two-sex time-varying kinship model expands on previous models by requiring:
+
+1. **Sex-specific mortality rates over time**: How survival differs between males and females across historical periods
+2. **Sex-specific fertility rates over time**: How fertility patterns differ between males and females across historical periods
+3. **Sex ratio at birth**: The proportion of births that are female
+4. **Sex of the focal individual**: Whether we're analyzing male or female kinship networks
+
+# Data Preparation {#model-input-2sex-time-varying}
+
+For this vignette, we'll use Swedish demographic data from the `DemoKin` package. However, since the package only includes female fertility and mortality data over time, we'll create synthetic male rates for illustration purposes.
+
+In a real-world application, you would ideally use actual male rates. Here, we'll create male rates by applying transformations to the female rates:
+
+```{r}
+# Extract dimensions of the data
+years <- ncol(swe_px)
+ages <- nrow(swe_px)
+
+# Use female rates directly from the package
+swe_surv_f_matrix <- swe_px
+swe_fert_f_matrix <- swe_asfr
+
+# Create synthetic male rates
+# Male survival: Lower than female (raising to power 1.5 reduces values)
+swe_surv_m_matrix <- swe_px ^ 1.5 # artificial perturbation for this example
+
+# Male fertility: Shifted to slightly older ages and slightly higher
+swe_fert_m_matrix <- rbind(matrix(0, 5, years),  
+                          swe_asfr[-((ages-4):ages),]) * 1.05 # artificial perturbation for this example
+```
+
+Let's examine the resulting rates for a specific year (1900) to verify they follow expected patterns:
+
+```{r}
+bind_rows(
+  data.frame(age = 0:100, sex = "Female", component = "Fertility rate", value = swe_fert_f_matrix[,"1900"]),
+  data.frame(age = 0:100, sex = "Male", component = "Fertility rate", value = swe_fert_m_matrix[,"1900"]),
+  data.frame(age = 0:100, sex = "Female", component = "Survival probability", value = swe_surv_f_matrix[,"1900"]),
+  data.frame(age = 0:100, sex = "Male", component = "Survival probability", value = swe_surv_m_matrix[,"1900"])) %>% 
+  ggplot(aes(age, value, col = sex)) +
+  geom_line() +
+  theme_bw() +
+  facet_wrap(~component, scales = "free") +
+  labs(
+    title = "Sex-specific demographic rates in Sweden (1900)",
+    x = "Age",
+    y = "Rate",
+    color = "Sex"
+  )
+```
+
+**Interpretation**: The plot confirms our synthetic rates follow expected patterns:
+
+- **Fertility**: Male fertility is shifted to slightly older ages compared to female fertility
+- **Survival**: Male survival probabilities are lower than female survival at all ages
+- **Both rates** show characteristic age patterns: fertility concentrated in reproductive ages, and survival declining with age
+
+# Running the Two-Sex Time-Varying Model {#run-model-2sex-time-varying}
+
+Now, let's implement the two-sex time-varying kinship model using the `kin2sex` function with `time_invariant = FALSE`:
+
+```{r}
+kin_out_time_varying <- kin2sex(
+  pf = swe_surv_f_matrix,       # Female survival matrix (age x year)
+  pm = swe_surv_m_matrix,       # Male survival matrix (age x year)
+  ff = swe_fert_f_matrix,       # Female fertility matrix (age x year)
+  fm = swe_fert_m_matrix,       # Male fertility matrix (age x year)
+  sex_focal = "f",              # Focal individual is female
+  time_invariant = FALSE,       # Use time-varying model
+  birth_female = .5,            # Sex ratio at birth (50% female)
+  output_cohort = 1900          # Focus on the 1900 birth cohort
+)
+```
+
+The resulting output provides detailed information on the kinship network for the 1900 female birth cohort, with relatives classified by both age and sex.
+
+# Comparing Time-Varying and Time-Invariant Models {#kin-availability-2sex-time-varying}
+
+To understand the impact of incorporating historical demographic change, let's compare the time-varying model with a time-invariant model that uses only 1900 rates:
+
+```{r, message=FALSE, warning=FALSE}
+# Run a time-invariant model using only 1900 rates
+kin_out_time_invariant <- kin2sex(
+  pf = swe_surv_f_matrix[,"1900"],    # Female survival (1900)
+  pm = swe_surv_m_matrix[,"1900"],    # Male survival (1900)
+  ff = swe_fert_f_matrix[,"1900"],    # Female fertility (1900) 
+  fm = swe_fert_m_matrix[,"1900"],    # Male fertility (1900)
+  sex_focal = "f",                    # Focal individual is female
+  birth_female = .5                   # Sex ratio at birth (50% female)
+)
+
+# Combine and plot the results
+kin_out_time_varying$kin_summary %>%
+  filter(cohort == 1900) %>% 
+  mutate(type = "variant") %>%
+  bind_rows(kin_out_time_invariant$kin_summary %>% mutate(type = "invariant")) %>% 
+  # Combine siblings and aunts/uncles for simplicity
+  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
+                         kin %in% c("ya", "oa") ~ "a",
+                         TRUE ~ kin)) %>%
+  # Select key relationships
+  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a")) %>%
+  # Group by relationship type, age, sex, and model type
+  group_by(type, kin, age_focal, sex_kin) %>%
+  summarise(count = sum(count_living)) %>%
+  # Create plot
+  ggplot(aes(age_focal, count, linetype = type)) +
+  geom_line() + 
+  theme_bw() +
+  facet_grid(cols = vars(kin), rows = vars(sex_kin), scales = "free") +
+  labs(
+    title = "Time-varying vs. time-invariant kinship models by sex",
+    subtitle = "Female 1900 birth cohort, Sweden",
+    x = "Age of focal individual",
+    y = "Number of living relatives",
+    linetype = "Model type"
+  )
+```
+
+**Interpretation**: This comparison reveals important differences between time-varying and time-invariant models:
+
+- **Relatives by sex**: The differences are pronounced for both male kin (bottom row) and female kin (top row), where mortality improvements over time led to greater availability than predicted by the time-invariant model
+- **Ascending generations**: For parents (m), grandparents (gm), and great-grandparents (ggm), the time-varying model shows higher kin availability at older ages, reflecting mortality improvements not captured by the time-invariant model
+- **Descendants**: For children (d), the time-varying model shows fewer kin, reflecting fertility decline over time
+- **Kin of same generation**: For siblings (s), fertility decline over time has limited impact on their numbers because these fertility events have either already occurred or will occur very shortly. Instead, mortality improvements play a more critical role, leading the time-varying model to predict a higher number of surviving siblings at older ages.
+
+The time-varying model captures the demographic transition that occurred over the 20th century, including declining fertility and mortality rates. This produces a more accurate representation of kinship dynamics for historical cohorts.
+
+# Approximation Methods for Limited Data {#approximations}
+
+In practice, demographic data is often limited, particularly for male fertility rates which can be difficult to obtain. Caswell [-@caswell_formal_2022] introduced two approximation methods to estimate two-sex kinship networks when male demographic rates are unavailable:
+
+1. **Androgynous approximation**: Assumes equal fertility and survival for males and females
+2. **GKP factors**: Applies multipliers to one-sex kin counts based on theoretical considerations in Goodman, Keyfitz and Pullum [-@caswell_formal_2022]
+
+Let's evaluate these approximations using French data from the `DemoKin` package:
+
+## Androgynous Approximation {#androgynous}
+
+The androgynous approximation uses female rates for both sexes. Let's compare it to the full two-sex model:
+
+```{r, message=FALSE, warning=FALSE}
+# Load data of France again
+fra_fert_f <- fra_asfr_sex[,"ff"]    # Female fertility rates
+fra_fert_m <- fra_asfr_sex[,"fm"]    # Male fertility rates
+fra_surv_f <- fra_surv_sex[,"pf"]    # Female survival probabilities
+fra_surv_m <- fra_surv_sex[,"pm"]    # Male survival probabilities
+
+# Full two-sex model
+kin_out <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, 
+                  sex_focal = "f", birth_female = .5)
+
+# Androgynous approximation
+kin_out_androgynous <- kin2sex(fra_surv_f, fra_surv_f, fra_fert_f, fra_fert_f, 
+                              sex_focal = "f", birth_female = .5)
+
+# Compare the results
+bind_rows(
+  kin_out$kin_summary %>% mutate(type = "full"),
+  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous")) %>%
+  group_by(kin, age_focal, sex_kin, type) %>%
+  summarise(count = sum(count_living)) %>%
+  ggplot(aes(age_focal, count, linetype = type)) +
+  geom_line() +
+  theme_bw() +
+  theme(legend.position = "bottom", axis.text.x = element_blank()) +
+  facet_grid(row = vars(sex_kin), col = vars(kin), scales = "free") +
+  labs(
+    title = "Androgynous approximation vs. full two-sex model",
+    subtitle = "France, 2012",
+    x = "Age of focal individual",
+    y = "Number of living relatives",
+    linetype = "Model type"
+  )
+```
+
+**Interpretation**: The androgynous approximation performs well for most kin types, particularly for female relatives. However, it shows noticeable discrepancies for most types of male relatives, where ignoring male-specific mortality leads to overestimation.
+
+## GKP Factors Approximation {#gkp}
+
+The GKP factors approach applies theoretical multipliers to one-sex kin counts. Let's implement and evaluate this approach:
+
+```{r, message=FALSE, warning=FALSE}
+# One-sex model
+kin_out_1sex <- kin(fra_surv_f, fra_fert_f, birth_female = .5)
+
+# Apply GKP factors
+kin_out_GKP <- kin_out_1sex$kin_summary %>%
+  mutate(count_living = case_when(
+    kin == "m" ~ count_living * 2,          # Parents: multiply by 2
+    kin == "gm" ~ count_living * 4,         # Grandparents: multiply by 4
+    kin == "ggm" ~ count_living * 8,        # Great-grandparents: multiply by 8
+    kin == "d" ~ count_living * 2,          # Children: multiply by 2
+    kin == "gd" ~ count_living * 4,         # Grandchildren: multiply by 4
+    kin == "ggd" ~ count_living * 4,        # Great-grandchildren: multiply by 4
+    kin == "oa" ~ count_living * 4,         # Older aunts/uncles: multiply by 4
+    kin == "ya" ~ count_living * 4,         # Younger aunts/uncles: multiply by 4
+    kin == "os" ~ count_living * 2,         # Older siblings: multiply by 2
+    kin == "ys" ~ count_living * 2,         # Younger siblings: multiply by 2
+    kin == "coa" ~ count_living * 8,        # Cousins (older): multiply by 8
+    kin == "cya" ~ count_living * 8,        # Cousins (younger): multiply by 8
+    kin == "nos" ~ count_living * 4,        # Nieces/nephews (older): multiply by 4
+    kin == "nys" ~ count_living * 4         # Nieces/nephews (younger): multiply by 4
+  ))
+
+# Compare approaches at selected ages
+bind_rows(
+  kin_out$kin_summary %>% mutate(type = "full"),
+  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous"),
+  kin_out_GKP %>% mutate(type = "gkp")) %>%
+  # Combine siblings, aunts/uncles, cousins, and nieces/nephews
+  mutate(kin = case_when(
+    kin %in% c("ys", "os") ~ "s",           # All siblings
+    kin %in% c("ya", "oa") ~ "a",           # All aunts/uncles
+    kin %in% c("coa", "cya") ~ "c",         # All cousins
+    kin %in% c("nys", "nos") ~ "n",         # All nieces/nephews
+    TRUE ~ kin)) %>%
+  # Select specific ages for comparison
+  filter(age_focal %in% c(5, 15, 30, 60, 80)) %>%
+  # Sum across sex for total kin counts
+  group_by(kin, age_focal, type) %>%
+  summarise(count = sum(count_living)) %>%
+  # Create bar chart
+  ggplot(aes(type, count)) +
+  geom_bar(aes(fill = type), stat = "identity") +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 90), legend.position = "bottom") +
+  facet_grid(col = vars(kin), row = vars(age_focal), scales = "free") +
+  labs(
+    title = "Comparison of approximation methods with full two-sex model",
+    subtitle = "France, 2012, at selected ages",
+    x = "Approximation method",
+    y = "Total number of living relatives",
+    fill = "Method"
+  )
+```
+
+**Interpretation**: This comparison shows that both approximation methods produce reasonable estimates for most kin types. Overall, these approximations offer practical alternatives when sex-specific data is limited, though they should be used with caution and their limitations understood.
+
+# Incorporating Causes of Death {#prevalance-2sex-time-varying}
+
+The `kin2sex` function also allows for the analysis of kinship networks by cause of death, providing insights into how different mortality causes affect kin availability. More details about kin bereavement methodology can be found in Caswell, Margolis and Verdery [-@Caswell2023]. Let's implement a simple cause-of-death model with two competing causes.
+
+Now assume we have two causes of death (COD). For females, the risk of the first COD is half the risk of the second COD for ages greater than 50. For males, the risk of the first COD is 2/3 of the second COD for ages greater than 50. We operationalize this using two matrices with dimension 2 by 101 (number of causes by number of ages).
+
+```{r}
+# Create matrices of relative risks by cause, sex, and age
+Hf <- matrix(c(.5, 1), nrow = 2, ncol = length(fra_surv_f))  # Female risk factors
+Hm <- matrix(c(.33, 1), nrow = 2, ncol = length(fra_surv_f)) # Male risk factors
+
+# Set equal risks for ages below 50
+Hf[,1:50] <- Hm[,1:50] <- 1
+```
+
+Now we'll run the two-sex model with cause of death information:
+
+```{r}
+kin_out_cod_invariant <- kin2sex(
+  pf = fra_surv_f,           # Female survival
+  pm = fra_surv_m,           # Male survival
+  ff = fra_fert_f,           # Female fertility
+  fm = fra_fert_m,           # Male fertility
+  Hf = Hf,                   # Female cause-specific risk factors
+  Hm = Hm,                   # Male cause-specific risk factors
+  time_invariant = TRUE      # Using time-invariant model for simplicity
+)
+```
+
+Let's examine the structure of the output:
+
+```{r}
+head(kin_out_cod_invariant)
+```
+
+The output now includes additional columns for each cause of death. Let's visualize the distribution of parental deaths by cause, sex, and age for a 30-year-old Focal:
+
+```{r}
+kin_out_cod_invariant %>% 
+  filter(kin == "m", age_focal == 30) %>% 
+  summarise(deadcause1 = sum(deadcause1),
+            deadcause2 = sum(deadcause2), .by = c(age_kin, sex_kin)) %>%
+  pivot_longer(deadcause1:deadcause2) %>% 
+  ggplot(aes(age_kin, value, col = sex_kin, linetype = name)) +
+  geom_line() + 
+  labs(
+    title = "Distribution of parental deaths by cause, sex, and age",
+    subtitle = "For a 30-year-old focal individual",
+    x = "Age of parent at death",
+    y = "Expected number of parental deaths",
+    color = "Sex of parent",
+    linetype = "Cause of death"
+  ) +
+  theme_bw()
+```
+
+**Interpretation**: This visualization shows the distribution of parental deaths by cause:
+
+- **Timing**: In this simplified example, all parental deaths occur after age 50
+- **Sex differences**: Male parents (fathers) show higher death counts at earlier ages, reflecting their higher mortality
+- **Cause differences**: The relative importance of different causes varies by sex, with males showing a different distribution than females
+
+This approach can be extended to include more causes of death and to incorporate time-varying cause-specific mortality, though this would require more complex data inputs.
+
+# Conclusion
+
+In this vignette, we've explored how to implement two-sex time-varying kinship models using the `DemoKin` package. These models provide a more comprehensive framework for understanding kinship dynamics by incorporating both sex differences and historical demographic change.
+
+Key insights include:
+
+1. Time-varying models capture the effects of demographic transition on kinship networks
+2. Sex-specific models reveal important differences in the availability of male versus female relatives
+3. Approximation methods offer practical alternatives when data is limited
+4. Cause-of-death extensions provide insights into how different mortality causes shape kinship structures
+
+These advanced models enhance our understanding of gender and family dynamics across demographic transitions, offering valuable tools for demographic analysis, care planning, and social policy development in aging societies.
+
+# References
+
diff --git a/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd b/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
new file mode 100644
index 0000000..c9828b3
--- /dev/null
+++ b/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
@@ -0,0 +1,315 @@
+---
+title: "One-sex time-invariant kinship model specified by age and stage"
+output:
+  html_document:
+    toc: true
+    toc_float: 
+      collapsed: false
+      smooth_scroll: true
+    theme: readable
+    highlight: pygments
+    number_sections: true
+    code_folding: show
+    df_print: paged
+    fig_caption: true
+bibliography: references.bib
+vignette: >
+  %\VignetteIndexEntry{One-sex time-invariant kinship model specified by age and stage}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include=FALSE}
+# Set up code chunk options
+knitr::opts_chunk$set(echo = TRUE,
+                      message = FALSE, 
+                      warning = FALSE, 
+                      fig.align = 'center',
+                      fig.width = 8,
+                      fig.height = 6,
+                      dpi = 300)
+# Prevent scientific notation (useful for the rate calculation)
+options(scipen = 999999)
+pkgload::load_all()
+```
+
+<style>
+/* Make TOC appear on the right side */
+.tocify {
+  position: fixed;
+  top: 50px;
+  right: 0;
+  width: 240px;
+  height: calc(100% - 50px);
+  overflow-y: auto;
+  margin-left: 0;
+  padding-left: 0;
+}
+
+/* Adjust main content to make room for right-side TOC */
+.main-container {
+  max-width: calc(100% - 260px);
+  margin-left: 10px;
+  margin-right: 250px;
+}
+
+/* Limit TOC to only show 2 levels */
+.tocify-subheader .tocify-subheader {
+  display: none;
+}
+
+/* Style for learning objectives box */
+.learning-objectives {
+  background-color: #e0f7fa;
+  border-left: 5px solid #00acc1;
+  padding: 15px;
+  margin-bottom: 20px;
+  border-radius: 3px;
+}
+</style>
+
+<div class="learning-objectives">
+<strong>Learning Objectives</strong>: In this vignette, you will learn how to extend the one-sex kinship model to incorporate stages alongside age. You will understand the implementation of multi-state matrix models, explore how demographic processes can vary by stage (e.g., parity), and analyze how these additional dimensions affect kinship structures.
+</div>
+
+# Introduction {#introduction}
+
+In previous vignettes, we explored kinship models where individuals were classified only by age. However, demographic processes are often influenced by other characteristics beyond age. For example, mortality and fertility rates may vary by marital status, education level, health condition, parity (number of children already born), or other socioeconomic factors.
+
+Multi-state kinship models address this limitation by incorporating both age and stage (additional states) in the analysis. These models allow us to:
+
+- Account for heterogeneity in mortality and fertility by stage
+- Track changes in stage over the life course (e.g., transitions between parity states)
+- Analyze kin availability by both age and stage
+- Understand how stage-specific demographic patterns shape family structures
+- Provide more nuanced estimates of kinship dynamics
+
+In this vignette, we will start from a simple model, **one-sex time-invariant multi-state kinship model**, outlined in Caswell [-@caswell_formal_2020], using the `DemoKin` package. We'll focus specifically on parity as our stage variable, which allows us to analyze how fertility history affects kinship networks.
+
+## Package Installation {#preparation}
+
+If you haven't already installed the required packages from the previous vignettes, here's what you'll need:
+
+```{r installs, eval=FALSE}
+# Install basic data analysis packages
+install.packages("dplyr")     # Data manipulation
+install.packages("tidyr")     # Data tidying
+install.packages("ggplot2")   # Data visualization
+install.packages("readr")     # Data import
+install.packages("knitr")     # Document generation
+install.packages("data.table")# Efficient data handling
+install.packages("Matrix")    # Matrix operations
+
+# Install DemoKin
+# DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
+# but we'll use the development version on GitHub (https://github.com/IvanWilli/DemoKin):
+install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)  # For kinship analysis
+```
+
+# Setting Up the Analysis Environment {#load-packages}
+
+Let's load the necessary packages for our analysis:
+
+```{r libraries, warning=F, message=FALSE}
+rm(list = ls())
+library(dplyr)    # For data manipulation
+library(tidyr)    # For restructuring data
+library(ggplot2)  # For visualization
+library(readr)    # For reading data
+library(knitr)    # For document generation
+```
+
+# Multi-State Kinship Models {#multi-state-models}
+
+## Understanding Stage-Structured Models {#understanding-stage-models}
+
+In traditional age-structured models, an individual's demographic rates depend only on their age. In multi-state models, we expand this framework to consider both age and stage, where "stage" represents another characteristic that influences demographic processes.
+
+Key components of multi-state models include:
+
+1. **Age-and-stage-specific mortality rates**: How survival probabilities vary by both age and stage
+2. **Age-and-stage-specific fertility rates**: How fertility varies by both age and stage
+3. **Age-specific transition probabilities**: How individuals move between stages at each age
+
+These components allow us to build more realistic models of population dynamics and kinship networks by accounting for heterogeneity beyond age.
+
+## Parity as a Stage Variable {#parity-models}
+
+In this vignette, we'll focus on **parity** (the number of children already born to a woman) as our stage variable. Parity is particularly relevant for kinship studies because:
+
+- Fertility rates often vary substantially by parity
+- A woman's ultimate family size affects her kinship network
+- Parity transitions follow clear rules (can only increase by integer values)
+- Parity status can influence other demographic processes like mortality
+
+The `DemoKin` package includes data from Slovakia in 1980, which we'll use to implement a parity-based kinship model.
+
+## Understanding the Data Structure {#data-structure}
+
+For multi-state models, we need several matrices that specify how demographic rates vary by both age and stage. Let's examine the structure of the Slovakia data included in the `DemoKin` package:
+
+```{r data_structure}
+# Examine fertility rates by age and parity
+head(svk_fxs[1:5, ])
+
+# Examine survival probabilities by age and parity
+head(svk_pxs[1:5, ])
+
+# Examine birth matrix (where newborns enter the population)
+head(svk_Hxs[1:5, ])
+
+# Look at the structure of the transition matrices
+typeof(svk_Uxs)
+length(svk_Uxs)
+svk_Uxs[[20]]  # Transition matrix for age 20
+```
+
+In this dataset:
+
+- `svk_fxs` is a data frame of fertility rates by age (rows) and parity stage (columns)
+- `svk_pxs` contains survival probabilities by age and parity
+- `svk_Hxs` specifies where newborns enter the population (in this case, at parity 0)
+- `svk_Uxs` is a list of matrices, one for each age, containing the probabilities of transitioning between parity states conditional on survival
+
+For parity, the stages represent:
+
+- Stage 1: Parity 0 (no children)
+- Stage 2: Parity 1 (one child)
+- Stage 3: Parity 2 (two children)
+- Stage 4: Parity 3 (three children)
+- Stage 5: Parity 4 (four children)
+- Stage 6: Parity 5+ (five or more children)
+
+Let's examine the transition matrix for a woman of reproductive age to understand how women move between parity states:
+
+```{r transition_matrix}
+# Display the transition matrix for age 25
+# This shows probabilities of moving between parity states
+svk_Uxs[[25]]
+```
+
+This matrix shows the probabilities of moving from one parity state (columns) to another (rows) for a 25-year-old woman, conditional on survival. Some key observations:
+
+- The matrix shows transitions from column j (starting parity) to row i (ending parity)
+- The diagonal elements represent the probability of remaining in the same parity state
+- Non-zero values appear only in the lower-triangular portion because parity can only increase (women can't "un-have" children)
+- Women at higher parities generally have lower probabilities of having another child
+
+# Implementing the Multi-State Model {#run-model-multi-state}
+
+Now let's implement the multi-state kinship model using the `kin_multi_stage` function:
+
+```{r}
+# Use birth_female=1 because fertility is for females only
+demokin_svk1980_caswell2020 <- 
+  kin_multi_stage(
+    U = svk_Uxs,            # List of transition matrices
+    f = svk_fxs,            # Fertility rates by age and parity
+    D = svk_pxs,            # Survival probabilities by age and parity
+    H = svk_Hxs,            # Birth matrix
+    birth_female = 1,       # All births are female (one-sex model)
+    parity = TRUE           # Stages represent parity states
+  )
+```
+
+This function computes the joint age-parity distribution of kin for a focal individual under the specified demographic conditions. The output includes information on both the age and parity state of each relative.
+
+# Analyzing Age and Parity Distributions {#age-and-parity-distribution}
+
+Let's examine how both age and parity are distributed among relatives. First, we'll look at the age-parity distribution of aunts when the focal individual is 20 and 60 years old:
+
+```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
+demokin_svk1980_caswell2020 %>% 
+  filter(kin %in% c("oa","ya"), age_focal %in% c(20,60)) %>% 
+  mutate(parity = as.integer(stage_kin)-1,
+         parity = case_when(parity == 5 ~ "5+", TRUE ~ as.character(parity))
+         ) %>% 
+  group_by(age_focal, age_kin, parity) %>% 
+  summarise(count = sum(living)) %>% 
+  ggplot() +
+  geom_bar(aes(x = age_kin, y = count, fill = parity), stat = "identity") +
+  geom_vline(aes(xintercept = age_focal), col = 2) +
+  labs(
+    title = "Age and parity distribution of aunts",
+    subtitle = "Slovakia, 1980",
+    x = "Age of aunt",
+    y = "Number of aunts",
+    fill = "Parity"
+  ) +
+  theme_bw() +
+  facet_wrap(~age_focal, nrow = 2, labeller = labeller(
+    age_focal = c("20" = "Focal age: 20", "60" = "Focal age: 60")
+  ))
+```
+
+**Interpretation**: These bar charts show the joint distribution of age and parity for aunts at two different focal ages:
+
+- **When Focal is 20** (upper panel): Aunts are mostly middle-aged (30s-50s) and concentrated in parities 2-3, reflecting the fertility patterns of that generation
+- **When Focal is 60** (lower panel): Aunts are much older (if still alive) and show a similar parity distribution, though with more high-parity individuals due to the fertility patterns of earlier cohorts
+
+The red vertical line indicates Focal's age, providing a reference point for comparing the ages of relatives. This joint distribution provides richer information than looking at age or parity alone.
+
+# Kin Counts by Parity Over the Life Course {#kin-by-parity}
+
+Now let's examine how the parity distribution of different types of relatives changes over Focal's life course. We'll focus on daughters and mothers:
+
+```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
+demokin_svk1980_caswell2020 %>% 
+  filter(kin %in% c("d","m")) %>% 
+  mutate(parity = as.integer(stage_kin)-1,
+         parity = case_when(parity == 5 ~ "5+", TRUE ~ as.character(parity))) %>% 
+  group_by(age_focal, kin, parity) %>% 
+  summarise(count = sum(living)) %>% 
+  DemoKin::rename_kin() %>% 
+  ggplot() +
+  geom_bar(aes(x = age_focal, y = count, fill = parity), stat = "identity") +
+  labs(
+    title = "Parity distribution of mothers and daughters over the life course",
+    subtitle = "Slovakia, 1980",
+    x = "Age of focal individual",
+    y = "Number of relatives",
+    fill = "Parity"
+  ) +
+  theme_bw() +
+  facet_wrap(~kin_label, nrow = 2)
+```
+
+**Interpretation**: These stacked bar charts reveal how the parity distribution of mothers and daughters evolves across Focal's life course:
+
+- **Mothers**: 
+  - Most mothers are in parity 2-3, reflecting the dominant family size in this population
+  - At Focal's birth (age 0), mothers are necessarily at parity 1 or higher (as they must have at least one child - the Focal individual)
+  - The mothers' parity distribution shows a gradual shift toward higher parities when Focal is young, as some mothers continue to have additional children
+  - The composition is relatively stable after Focal reaches adulthood, with slight changes due to differential mortality by parity
+
+- **Daughters**:
+  - Initially all daughters are in parity 0 (childless)
+  - As Focal ages, daughters transition to higher parity states
+  - By the time Focal reaches old age, the parity distribution of daughters resembles the overall population pattern
+  - The total number increases until Focal's reproductive years end, then remains stable
+
+These patterns highlight the intergenerational transmission of fertility behaviors and how demographic patterns ripple through kinship networks.
+
+# Conclusion
+
+In this vignette, we've explored how to implement one-sex time-invariant multi-state kinship models using the `DemoKin` package. By incorporating both age and stage (parity) in our analysis, we've gained richer insights into the structure of kinship networks than would be possible with age alone.
+
+Key insights include:
+
+1. Demographic processes vary not only by age but also by other characteristics like parity
+2. Multi-state models allow us to track the joint distribution of age and stage among relatives
+3. The parity distribution of relatives evolves in complex ways over the life course
+4. Stage transitions (e.g., between parity states) are a key component of kinship dynamics
+
+While we focused on parity in this vignette, the `kin_multi_stage` function can be used for any state variable by setting the parameter `parity = FALSE` (the default). This flexibility opens up numerous applications:
+
+1. **Health status transitions**: Analyzing how health conditions affect and are affected by kinship networks
+2. **Educational attainment**: Exploring how education levels influence family formation and structure
+3. **Marital status**: Incorporating marriage, divorce, and widowhood into kinship dynamics
+4. **Geographical location**: Modeling proximity and migration within kinship networks
+5. **Labor force participation**: Understanding how work patterns interact with family structures
+
+# References
+
diff --git a/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd b/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
new file mode 100644
index 0000000..e8d4fdc
--- /dev/null
+++ b/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
@@ -0,0 +1,661 @@
+---
+title: "Two-sex time-varying kinship model specified by age and stage"
+output:
+  html_document:
+    toc: true
+    toc_float: 
+      collapsed: false
+      smooth_scroll: true
+    theme: readable
+    highlight: pygments
+    number_sections: true
+    code_folding: show
+    df_print: paged
+    fig_caption: true
+bibliography: references.bib
+vignette: >
+  %\VignetteIndexEntry{Two-sex time-varying kinship model specified by age and stage}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include=FALSE}
+# Set up code chunk options
+knitr::opts_chunk$set(echo = TRUE,
+                      message = FALSE, 
+                      warning = FALSE, 
+                      fig.align = 'center',
+                      fig.width = 8,
+                      fig.height = 6,
+                      dpi = 300)
+# Prevent scientific notation (useful for the rate calculation)
+options(scipen = 999999)
+pkgload::load_all()
+```
+
+<style>
+/* Make TOC appear on the right side */
+.tocify {
+  position: fixed;
+  top: 50px;
+  right: 0;
+  width: 240px;
+  height: calc(100% - 50px);
+  overflow-y: auto;
+  margin-left: 0;
+  padding-left: 0;
+}
+
+/* Adjust main content to make room for right-side TOC */
+.main-container {
+  max-width: calc(100% - 260px);
+  margin-left: 10px;
+  margin-right: 250px;
+}
+
+/* Limit TOC to only show 2 levels */
+.tocify-subheader .tocify-subheader {
+  display: none;
+}
+
+/* Style for learning objectives box */
+.learning-objectives {
+  background-color: #e0f7fa;
+  border-left: 5px solid #00acc1;
+  padding: 15px;
+  margin-bottom: 20px;
+  border-radius: 3px;
+}
+</style>
+
+<div class="learning-objectives">
+<strong>Learning Objectives</strong>: In this vignette, you will learn how to implement a kinship model that combines both two-sex and time-varying approaches with multiple stages. You will understand how to incorporate sex-specific demographic rates that change over time, analyze the effects of demographic transitions on kinship structures by sex and stage, and explore applications with different stage variables such as parity and education.
+</div>
+
+# Introduction {#introduction}
+
+In this final vignette, we integrate all elements from previous tutorials into the most comprehensive kinship model available: a **two-sex time-varying multi-state model**. This framework simultaneously accounts for:
+
+- Sex differences in demographic rates
+- Historical changes over time
+- Stage-based transitions (like parity or education)
+- Joint distributions of age, sex, and stage among relatives
+
+Building on Caswell and colleagues' theoretical developments [-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2021; -@caswell_formal_2022], this approach provides unprecedented analytical power for understanding complex family structures. We'll implement this comprehensive model using the `kin_multi_stage_time_variant_2sex` function, exploring two examples: one using parity and another using educational attainment to demonstrate how these models illuminate contemporary family dynamics in ways simpler models cannot.
+
+## Package Installation {#preparation}
+
+If you haven't already installed the required packages from the previous vignettes, here's what you'll need:
+
+```{r installs, eval=FALSE}
+# Install basic data analysis packages
+install.packages("dplyr")     # Data manipulation
+install.packages("tidyr")     # Data tidying
+install.packages("ggplot2")   # Data visualization
+install.packages("readr")     # Data import
+install.packages("knitr")     # Document generation
+install.packages("data.table")# Efficient data handling
+install.packages("Matrix")    # Matrix operations
+
+# Install DemoKin
+# DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
+# but we'll use the development version on GitHub (https://github.com/IvanWilli/DemoKin):
+install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)
+```
+
+# Setting Up the Analysis Environment {#load-packages}
+
+Let's load the necessary packages for our analysis:
+
+```{r libraries, warning=F, message=FALSE}
+rm(list = ls())
+library(dplyr)    # For data manipulation
+library(tidyr)    # For restructuring data
+library(ggplot2)  # For visualization
+library(readr)    # For reading data
+library(knitr)    # For document generation
+library(Matrix)   # For matrix operations
+library(tictoc)   # For timing operations
+options(dplyr.summarise.inform = FALSE) # hide summarise output
+# source("kin_multi_stage_time_variant_2sex.R")
+```
+
+# Two-Sex Time-Varying Multi-State Models {#two-sex-time-varying-multi-state}
+
+The `kin_multi_stage_time_variant_2sex` function in the `DemoKin` package allows us to implement a comprehensive kinship model that accounts for sex, time, and stage simultaneously. This function computes stage-specific kinship networks across both sexes for an average member of a population (focal) under time-varying demographic rates.
+
+The model estimates:
+
+- The number of relatives of each type
+- The age distribution of relatives
+- The sex distribution of relatives
+- The stage distribution of relatives
+- How these distributions change over the life course of Focal
+- How they vary by Focal's birth cohort
+
+## Model Components and Requirements {#model-requirements}
+
+The two-sex time-varying multi-state model requires several inputs:
+
+1. **Sex-specific mortality rates by stage over time**: How survival probabilities differ between males and females of different stages across historical periods
+2. **Sex-specific fertility rates by stage over time**: How fertility patterns differ between males and females of different stages across historical periods
+3. **Sex-specific transition rates between stages over time**: How individuals move between stages at each age, potentially differing by sex
+4. **Sex ratio at birth**: The proportion of births that are female
+5. **Birth redistribution matrices**: How newborns are distributed across stages
+6. **Sex and initial stage of the focal individual**: The characteristics of the person whose kin network we're analyzing
+
+In the following sections, we'll explore two examples of this model using different stage variables: parity and education.
+
+# Example 1: Parity as the Stage Variable {#parity-as-stage}
+
+## Data Preparation {#parity-data-preparation}
+
+In our first example, we'll use parity (number of children already born) as our stage variable. We'll use data from the United Kingdom ranging from 1965 to 2022, sourced from the Human Mortality Database and the Office for National Statistics.
+
+Due to data limitations, we make some simplifying assumptions:
+
+1. Fertility rates vary with time and parity but are the same across sexes (the "androgynous approximation")
+2. Mortality rates vary with time and sex but are the same across parity classes
+3. Parity progression probabilities vary with time but are the same across sexes
+
+Let's load the pre-processed UK data:
+
+```{r parity_data_load, message=FALSE, warning=FALSE}
+# Load pre-processed data for UK
+F_mat_fem <- Female_parity_fert_list_UK       # Female fertility by parity
+F_mat_male <- Female_parity_fert_list_UK      # Male fertility (same as female due to androgynous approximation)
+T_mat_fem <- Parity_transfers_by_age_list_UK  # Female parity transitions
+T_mat_male <- Parity_transfers_by_age_list_UK # Male parity transitions (same as female)
+U_mat_fem <- Female_parity_mortality_list_UK  # Female survival
+U_mat_male <- Male_parity_mortality_list_UK   # Male survival
+H_mat <- Redistribution_by_parity_list_UK     # Birth redistribution matrices
+```
+
+These lists contain period-specific demographic rates:
+
+- `U_mat_fem`/`U_mat_male`: Lists of matrices containing survival probabilities by age (rows) and parity (columns) from 1965-2022
+- `F_mat_fem`/`F_mat_male`: Lists of matrices containing fertility rates by age and parity 
+- `T_mat_fem`/`T_mat_male`: Lists of transition matrices showing probabilities of moving between parity states
+- `H_mat`: List of matrices that redistribute newborns to age-class 1 and parity 0
+
+## Running the Parity Model {#parity-model-running}
+
+Now let's implement the two-sex time-varying multi-state model with parity as the stage variable:
+
+```{r parity_model, message=FALSE, warning=FALSE, eval=TRUE}
+# Define time period and parameters
+no_years <- 40  # Run the model for 40 years (1965-2005)
+
+# Run the model
+kin_out_1965_2005 <-
+  kin_multi_stage_time_variant_2sex(
+    U_list_females = U_mat_fem[1:(1+no_years)],        # Female survival matrices
+    U_list_males = U_mat_male[1:(1+no_years)],         # Male survival matrices
+    F_list_females = F_mat_fem[1:(1+no_years)],        # Female fertility matrices
+    F_list_males = F_mat_male[1:(1+no_years)],         # Male fertility matrices
+    T_list_females = T_mat_fem[1:(1+no_years)],        # Female transition matrices
+    T_list_males = T_mat_fem[1:(1+no_years)],          # Male transition matrices
+    H_list = H_mat[1:(1+no_years)],                    # Birth redistribution matrices
+    birth_female = 1 - 0.51,                           # UK sex ratio (49% female)
+    parity = TRUE,                                      # Stages represent parity
+    output_kin = c("d", "oa", "ys", "os"),             # Selected kin types
+    summary_kin = TRUE,                                 # Produce summary statistics
+    sex_Focal = "Female",                               # Focal is female
+    initial_stage_Focal = 1,                            # Focal starts at parity 0
+    # model_years <- seq(1965, 2005, 5),                     # the sequence of years we model
+    output_years = c(1965, 1975, 1985, 1995, 2005)     # Selected output years
+  )
+```
+
+> Note: This model run takes approximately 30 minutes to complete. 
+
+Now we need to recode the stage variables to show meaningful parity labels:
+
+```{r parity_output_recode, message=FALSE, warning=FALSE, eval=TRUE}
+# After running the model, recode the parity stage values
+kin_out_1965_2005$kin_summary <- 
+  kin_out_1965_2005$kin_summary %>%
+  mutate(stage_kin = factor(as.numeric(stage_kin) - 1, 
+                           levels = c(0, 1, 2, 3, 4, 5),
+                           labels = c("0", "1", "2", "3", "4", "5+")))
+
+# Do the same for the kin_full dataframe if you're using it
+kin_out_1965_2005$kin_full <- 
+  kin_out_1965_2005$kin_full %>%
+  mutate(stage_kin = factor(as.numeric(stage_kin) - 1, 
+                           levels = c(0, 1, 2, 3, 4, 5),
+                           labels = c("0", "1", "2", "3", "4", "5+")))
+```
+
+## Analyzing Kin Counts by Parity {#parity-kin-counts}
+
+Let's examine the structure of the output:
+
+```{r parity_output_show, message=FALSE, warning=FALSE, eval=TRUE}
+head(kin_out_1965_2005$kin_summary)
+```
+
+The output includes:
+
+- `age_focal`: Age of the focal individual
+- `kin_stage`: Stage (parity) of the relatives
+- `sex_kin`: Sex of the relatives
+- `year`: Calendar year of observation
+- `group`: Type of relative (d = children, oa = older aunts/uncles, etc.)
+- `count`: Expected number of living relatives
+- `cohort`: Birth cohort of the focal individual
+
+### Period Analysis of Kin by Parity {#parity-period-analysis}
+
+Let's visualize the distribution of older aunts and uncles by parity for different ages of Focal across different calendar years.
+
+We first restrict Focal's kinship network to aunts and uncles older than Focal's mother by setting `group` == "oa". We visualize the marginal parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different color schemes. Implicit in the below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
+
+```{r parity_aunts_uncles, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_1965_2005$kin_summary %>%
+  filter(group == "oa") %>%
+  ggplot(aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
+  geom_bar(position = "stack", stat = "identity") +
+  facet_grid(sex_kin ~ year) +
+  scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
+  labs(
+    title = "Parity distribution of older aunts and uncles by Focal's age",
+    subtitle = "United Kingdom, 1965-2005",
+    x = "Age of focal individual",
+    y = "Number of older aunts and uncles",
+    fill = "Parity",
+    color = "Parity"
+  )
+```
+
+We could also consider any other kin in Focal's network, for instance, offspring using `group` == "d":
+
+```{r parity_offspring, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_1965_2005$kin_summary %>%
+  filter(group == "d") %>%
+  ggplot(aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
+  geom_bar(position = "stack", stat = "identity") +
+  facet_grid(sex_kin ~ year) +
+  scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
+  labs(
+    title = "Parity distribution of children by Focal's age",
+    subtitle = "United Kingdom, 1965-2005",
+    x = "Age of focal individual",
+    y = "Number of children",
+    fill = "Parity",
+    color = "Parity"
+  )
+```
+
+### Cohort Analysis of Kin by Parity {#parity-cohort-analysis}
+
+Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific.
+
+We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring and only show Focals born of `cohort` 1910, 1925, or 1965:
+
+```{r parity_cohort, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_1965_2005$kin_summary %>%
+  filter(group == "d", cohort %in% c(1910, 1925, 1965)) %>%
+  ggplot(aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
+  geom_bar(position = "stack", stat = "identity") +
+  facet_grid(sex_kin ~ cohort) +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
+  labs(
+    title = "Parity distribution of offspring by birth cohort",
+    subtitle = "United Kingdom, ages observed between 1965-2005",
+    x = "Age of focal individual",
+    y = "Number of offspring",
+    fill = "Parity",
+    color = "Parity"
+  )
+```
+
+**Interpretation**:
+
+- The 1910 cohort (observed at ages 55-95): Most offspring have already completed their childbearing, showing a mix of parity states
+- The 1925 cohort (observed at ages 40-80): Offspring are observed during and after their reproductive years
+- The 1965 cohort (observed at ages 0-40): Initially, all offspring are in parity 0, gradually transitioning to higher parities as they age
+
+## Age Distribution of Kin by Parity {#parity-age-distribution}
+
+For more detailed analysis, we can examine the age and parity distribution of specific relatives. Let's look at the younger siblings of a 50-year-old Focal across different years:
+
+```{r parity_siblings_young, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_1965_2005$kin_full %>%
+  filter(group == "ys",
+         age_focal == 50) %>%
+  ggplot(aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
+  geom_bar(position = "stack", stat = "identity") +
+  facet_grid(sex_kin ~ year) +
+  scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
+  labs(
+    title = "Age and parity distribution of younger siblings",
+    subtitle = "For a 50-year-old focal individual, United Kingdom, 1965-2005",
+    x = "Age of sibling",
+    y = "Number of younger siblings",
+    fill = "Parity",
+    color = "Parity"
+  )
+```
+
+Notice the discontinuity along the x-axis at age 50. This reflects the fact that younger siblings cannot be older than Focal (by definition). Similarly, when we examine older siblings, we'll see they cannot be younger than Focal.
+
+With a simple manipulation of the output data frame, we can also plot the age and parity distribution of all siblings combined:
+
+```{r parity_siblings_all, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_1965_2005$kin_full %>%
+  filter((group == "ys" | group == "os"),
+         age_focal == 50) %>%
+  pivot_wider(names_from = group, values_from = count) %>%
+  mutate(count = `ys` + `os`) %>%
+  ggplot(aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
+  geom_bar(position = "stack", stat = "identity") +
+  facet_grid(sex_kin ~ year) +
+  scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  theme_bw() +
+  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
+  labs(
+    title = "Age and parity distribution of all siblings",
+    subtitle = "For a 50-year-old focal individual, United Kingdom, 1965-2005",
+    x = "Age of sibling",
+    y = "Number of siblings",
+    fill = "Parity",
+    color = "Parity"
+  )
+```
+
+# Example 2: Education as the Stage Variable {#education-as-stage}
+
+## Data Preparation {#education-data-preparation}
+
+In our second example, we'll use educational attainment as our stage variable. The data is for Singapore ranging from 2020 to 2090, sourced from the Wittgenstein Center. The data is aggregated into 5-year age groups and 5-year time intervals.
+
+Some simplifying assumptions we make due to data availability:
+
+1. Fertility rates vary with time and education but are identical for both sexes (androgynous approximation)
+2. Age-specific education transition probabilities vary over time but not by sex
+3. Educational transitions for young children follow Singapore's Compulsory Education Act 
+4. Demographic rates before 2020 are assumed stable (time-invariant)
+
+Let's load the pre-processed Singapore data:
+
+This data includes:
+
+- `U_mat_fem_edu`/`U_mat_male_edu`: Lists of matrices containing survival probabilities by age (rows) and education (columns) from 2020-2090
+- `F_mat_fem_edu`/`F_mat_male_edu`: Lists of matrices containing fertility rates by age and education
+- `T_mat_fem_edu`/`T_mat_male_edu`: Lists of transition matrices showing probabilities of moving between education states
+- `H_mat_edu`: List of matrices that redistribute newborns to age-class 1 and "no education" category
+
+Before running the model, let's examine some trends in the data:
+
+```{r edu_trends, message=FALSE, warning=FALSE, eval=TRUE}
+# Calculate and plot Total Fertility Rate by education level over time
+tfr_data <- lapply(seq_along(F_mat_fem_edu), function(i) {
+  col_sums <- colSums(F_mat_fem_edu[[i]])
+  data.frame(
+    year = 2020 + (i - 1) * 5,
+    education = factor(colnames(F_mat_fem_edu[[i]]), 
+                      levels = c("e1", "e2", "e3", "e4", "e5", "e6"),
+                      labels = c("no education", "incomplete primary", 
+                                "primary", "lower secondary", 
+                                "upper secondary", "post-secondary")),
+    tfr = col_sums
+  )
+})
+
+tfr_df <- do.call(rbind, tfr_data)
+
+# Plot TFR trends
+ggplot(tfr_df, aes(x = year, y = tfr, color = education, group = education)) +
+  geom_line(size = 1) +
+  geom_point() +
+  theme_minimal() +
+  labs(
+    title = "Total Fertility Rate by Education Over Time",
+    x = "Year",
+    y = "TFR",
+    color = "Education"
+  )
+```
+
+## Running the Education Model {#education-model-running}
+
+Now let's implement the two-sex time-varying multi-state model with education as the stage variable:
+
+```{r education_model, message=FALSE, warning=FALSE, eval=TRUE}
+# Define time period and parameters
+time_range <- seq(2020, 2090, 5)
+no_years <- length(time_range) - 1
+output_year <- seq(1, no_years + 1, 1)
+
+# Run the model
+kin_out_2020_2090 <-
+  kin_multi_stage_time_variant_2sex(
+    U_list_females = U_mat_fem_edu[1:(1+no_years)],     # Female survival matrices
+    U_list_males = U_mat_male_edu[1:(1+no_years)],      # Male survival matrices
+    F_list_females = F_mat_fem_edu[1:(1+no_years)],     # Female fertility matrices
+    F_list_males = F_mat_male_edu[1:(1+no_years)],      # Male fertility matrices
+    T_list_females = T_mat_fem_edu[1:(1+no_years)],     # Female transition matrices
+    T_list_males = T_mat_fem_edu[1:(1+no_years)],       # Male transition matrices
+    H_list = H_mat_edu[1:(1+no_years)],                 # Birth redistribution matrices
+    birth_female = 1/(1.06+1),                          # Singapore sex ratio (48.5% female)
+    parity = FALSE,                                      # Stages represent education
+    summary_kin = TRUE,                                  # Produce summary statistics
+    sex_Focal = "Female",                                # Focal is female
+    initial_stage_Focal = 1,                             # Focal starts with no education
+    # model_years = output_year,                            # the sequence of years we model
+    output_years = output_year                           # All years
+  )
+```
+
+> Note: This model run takes approximately 3 minutes to complete.
+
+Now we need to recode the stage variables to show meaningful educational labels and convert years/ages to real values (since we used 5-year intervals):
+
+```{r education_recode, message=FALSE, warning=FALSE, eval=TRUE}
+# Recode year and age variables to show correct values
+kin_out_2020_2090$kin_summary <- 
+  kin_out_2020_2090$kin_summary %>%
+  mutate(year = (year-1)*5+min(time_range),
+         age_focal = age_focal*5,
+         cohort = year - age_focal,
+         stage_kin = factor(stage_kin, levels = c(1, 2, 3, 4, 5, 6),
+                       labels = c(
+                         "no education",
+                         "incomplete primary",
+                         "primary",
+                         "lower secondary",
+                         "upper secondary",
+                         "post-secondary"
+                       )))
+
+kin_out_2020_2090$kin_full <- 
+  kin_out_2020_2090$kin_full %>%
+  mutate(year = (year-1)*5+min(time_range),
+         age_focal = age_focal*5,
+         age_kin = age_kin*5,
+         cohort = year - age_focal,
+         stage_kin = factor(stage_kin, levels = c(1, 2, 3, 4, 5, 6),
+                       labels = c(
+                         "no education",
+                         "incomplete primary",
+                         "primary",
+                         "lower secondary",
+                         "upper secondary",
+                         "post-secondary"
+                       )))
+```
+
+## Analyzing Kin by Educational Attainment {#education-kin-analysis}
+
+### Cohort Analysis of Kin by Education {#education-cohort-analysis}
+
+First, let's visualize the total number of living kin by educational attainment for a woman born in 2020 in Singapore:
+
+```{r education_total_kin, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_2020_2090$kin_summary %>%
+  # Exclude Focal from the analysis
+  filter(group != "Focal") %>% 
+  filter(cohort == 2020) %>%
+  rename(kin = group) %>% 
+  # rename_kin(sex = "2sex") %>% 
+  summarise(count = sum(count), .by = c(stage_kin, age_focal)) %>%
+  ggplot(aes(x = age_focal, y = count, fill = stage_kin)) +
+  geom_area(colour = "black") +
+  labs(
+    title = "Total living kin by educational attainment over the life course",
+    subtitle = "For a woman born in 2020, Singapore",
+    y = "Expected number of living kin",
+    x = "Age of focal individual",
+    fill = "Educational attainment"
+  ) +
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
+
+Now let's look at specific types of relatives:
+
+```{r education_specific_kin, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_2020_2090$kin_summary %>%
+  filter(group %in% c("d", "gd", "m", "gm")) %>% 
+  filter(cohort == 2020) %>%
+  rename(kin = group) %>% 
+  # rename_kin(sex = "2sex") %>% 
+  summarise(count = sum(count), .by = c(kin, stage_kin, age_focal)) %>%
+  ggplot(aes(x = age_focal, y = count, fill = stage_kin)) +
+  geom_area(colour = "black") +
+  labs(
+    title = "Living kin by educational attainment over the life course",
+    subtitle = "For a woman born in 2020, Singapore",
+    y = "Expected number of living kin",
+    x = "Age of focal individual",
+    fill = "Educational attainment"
+  ) +
+  facet_wrap(. ~ kin) +
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
+
+**Interpretation** This visualization captures how educational expansion transforms family networks, showing the increasing educational diversity across generations in Singapore:
+
+- The total number of living relatives peaks around age 40-50
+- Relatives with upper secondary education (blue) constitute the largest proportion of the kinship network
+- The educational composition of relatives changes dynamically across the life course
+- At younger ages, relatives have lower educational attainment. As Focal ages, the proportion of higher-educated relatives (green, yellow, and red categories) increases
+
+Let's examine the age and educational distribution of key relatives when the focal individual is 60 years old:
+
+```{r education_age_distribution, message=FALSE, warning=FALSE, fig.height=6, fig.width=10, eval=TRUE}
+kin_out_2020_2090$kin_full %>%
+  filter(
+    group %in%  c("d", "gd", "m", "gm"),
+    age_focal == 60,
+    cohort == 2020
+  ) %>%
+  rename(kin = group) %>% 
+  # rename_kin("2sex") %>% 
+  ggplot(aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
+  geom_bar(position = "stack", stat = "identity") +
+  scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
+  labs(
+    title = "Age and educational distribution of key relatives",
+    subtitle = "For a 60-year-old woman born in 2020, Singapore",
+    x = "Age of relative",
+    y = "Number of relatives",
+    fill = "Educational attainment",
+    color = "Educational attainment"
+  ) +
+  facet_wrap(. ~ kin) +
+  theme_bw() +
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5),
+    legend.position = "bottom"
+  )
+```
+
+**Interpretation**: This visualization reveals the evolving educational composition of different types of relatives across the life course for a woman born in 2020 in Singapore:
+
+- **Children** (d): Educational attainment increases with Focal's age, showing a shift towards higher education levels
+- **Grandchildren** (gd): Emerge later in life with a more diverse educational profile
+- **Grandparents** (gm): Predominantly have upper secondary (blue) and lower secondary (teal) education
+- **Parents** (m): Similar to grandparents, with a concentration of upper secondary education
+
+### Period Analysis of Kin by Education {#education-period-analysis}
+
+Now let's examine how the educational composition of specific relatives changes over time. First, let's look at the educational attainment of descendants (children and grandchildren) of 60-year-old women across different calendar years:
+
+```{r education_descendants_time, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+kin_out_2020_2090$kin_summary %>%
+  filter(
+    group %in% c("d", "gd"),
+    age_focal == 60
+  ) %>% 
+  rename(kin = group) %>% 
+  # rename_kin(sex = "2sex") %>% 
+  summarise(count = sum(count), .by = c(stage_kin, kin, year)) %>%
+  ggplot(aes(x = year, y = count, fill = stage_kin)) +
+  geom_area(colour = "black") +
+  labs(
+    title = "Educational attainment of descendants over time",
+    subtitle = "For women aged 60, Singapore, 2020-2090",
+    y = "Expected number of descendants",
+    x = "Year",
+    fill = "Educational attainment"
+  ) +
+  facet_grid(. ~ kin) +
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
+
+**Interpretation**:
+
+- **Children** (d): Concentrated between ages 30-50, with upper secondary education (blue) being most prevalent
+- **Grandchildren** (gd): Mostly young (under 20), with emerging educational diversity
+- **Grandparents** (gm): No longer living at this point
+- **Parents** (m): Clustered around ages 80-90, with upper secondary and lower secondary education dominating
+
+# Limitations and Assumptions {#limitations}
+
+When implementing these models, we've made several simplifying assumptions due to data limitations:
+
+For the UK parity model:
+
+1. Fertility rates vary with time and parity but are the same across sexes
+2. Mortality rates vary with time and sex but are the same across parity classes
+3. Parity progression probabilities vary with time but are the same across sexes
+
+For the Singapore education model:
+
+1. Fertility rates vary over time and by education but are identical for both sexes
+2. Age-specific education transition probabilities vary over time but not by sex
+3. Educational transitions for young children follow Singapore's Compulsory Education Act
+4. Demographic rates before 2020 are assumed stable (time-invariant)
+
+These assumptions should be kept in mind when interpreting the results.
+
+# Conclusion
+
+In this vignette, we've implemented two-sex time-varying multi-state kinship models—the most comprehensive demographic kinship framework currently available. By integrating age, sex, time, and stage dimensions, these models provide unprecedented insights into family structures and their evolution.
+
+Our two examples demonstrated the analytical power of this integrated approach:
+
+- The parity example revealed how reproductive patterns shape family structures differently for males and females across historical periods
+- The education example showed how educational expansion transforms kinship networks, creating increasingly educated family systems over time
+
+These comprehensive models have numerous applications across disciplines:
+
+- Understanding intergenerational transmission of socioeconomic status
+- Analyzing care needs and support networks in diverse populations
+- Projecting how family structures might evolve under different demographic scenarios
+- Exploring how education, health, marriage, and other characteristics intersect with kinship
+
+# References
diff --git a/vignettes/Reference_OneSex.Rmd b/vignettes/Reference_OneSex.Rmd
deleted file mode 100644
index 16662a5..0000000
--- a/vignettes/Reference_OneSex.Rmd
+++ /dev/null
@@ -1,270 +0,0 @@
----
-title: "Expected kin counts by type of relative in a one-sex framework"
-bibliography: references.bib
-output: 
-  html_document:
-    toc: true
-    toc_depth: 1
-vignette: >
-  %\VignetteIndexEntry{Reference_OneSex}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, eval = F, include=FALSE}
-knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
-# library(devtools); load_all()
-```
-
-In this vignette, we'll demonstrate how `DemoKin` can be used to compute kinship networks for an average member of a given (female) population. Let us call her Focal: an average Swedish woman who has always lived in Sweden and whose family has never left the country.
-Here, we'll show how `DemoKin` can be used to compute the number and age distribution of Focal's relatives under a range of assumptions, including living and deceased kin. 
-
-## 1. Kin counts with time-invariant rates
-
-First, we compute kin counts in a **time-invariant** framework. We assume that Focal and all of her relatives experience the 2015 mortality and fertility rates throughout their entire lives [@caswell_formal_2019]. The `DemoKin` package includes data from Sweden as an example: age-by-year matrices of survival probabilities (*swe_px*), survival ratios (*swe_Sx*), fertility rates (*swe_asfr*), and population numbers (*swe_pop*). You can see the data contained in `DemoKin` with `data(package="DemoKin")`. This data comes from the [Human Mortality Database](https://www.mortality.org/) and [Human Fertility Database](https://www.humanfertility.org/) (see `?DemoKin::get_HMDHFD`). 
-
-In order to implement the time-invariant models, the function `DemoKin::kin` expects a vector of survival ratios and another vector of fertility rates. In this example, we get the data for the year 2015, and run the matrix models:
-
-```{r, message=FALSE, warning=FALSE}
-pkgload::load_all()
-library(tidyr)
-library(dplyr)
-library(ggplot2)
-library(knitr)
-# First, get vectors for a given year
-swe_surv_2015 <- swe_px[,"2015"]
-swe_asfr_2015 <- swe_asfr[,"2015"]
-# Run kinship models
-swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
-```
-
-### 1.1. Value
-
-`DemoKin::kin()` returns a list containing two data frames: `kin_full` and `kin_summary`. 
-
-`kin_full` contains expected kin counts by year (or cohort), age of Focal and age of kin. Note that the columns `year` and `cohort` are empty if the argument is `time_invariant = TRUE` in `kin` (as in this example).
-
-```{r}
-head(swe_2015$kin_full)
-```
-
-`kin_summary` is a ‘summary’ data frame derived from `kin_full`. 
-
-```{r}
-head(swe_2015$kin_summary)
-```
-
-To produce it, we sum over all ages of kin to produce a data frame of expected kin counts by year or cohort and age of Focal (but not by age of kin). 
-Consider this simplified example for living kin counts:
-
-```{r, message=FALSE, warning=FALSE}
-kin_summary_example <- 
-  swe_2015$kin_full %>% 
-  select(year, cohort, kin, age_focal, age_kin, living, dead) %>% 
-  group_by(year, cohort, kin, age_focal) %>% 
-  summarise(count_living = sum(living)) 
-
-head(kin_summary_example)
-```
-
-### 1.2. Visualizing the distribution of kin
-
-Let us now visualize the distribution of relatives over Focal's lifecourse using the summary data.frame `kin_summary`:
-
-```{r, fig.height=6, fig.width=8}
-swe_2015[["kin_summary"]] %>%
-  ggplot() +
-  geom_line(aes(age_focal, count_living)) +
-  theme_bw() +
-  labs(y = "Expected number of living relatives") +
-  facet_wrap(~kin)
-```
-
-Here, each relative type is identified by a unique code. Note that `DemoKin` uses different codes than Caswell [-@caswell_formal_2019]; the equivalence between the two set of codes is given in the following table:
-
-```{r, fig.height=6, fig.width=8, echo=FALSE}
-demokin_codes %>% 
-  kable
-```
-
-We can also visualize the age distribution of relatives when Focal is 35 years old (now, with full names to identify each relative type using the function `DemoKin::rename_kin()`):
-
-```{r, fig.height=6, fig.width=8}
-swe_2015[["kin_full"]] %>%
-  filter(age_focal == 35) %>% 
-  ggplot() +
-  geom_line(aes(age_kin, living))  +
-  geom_vline(xintercept = 35, color=2) +
-  labs(y = "Expected number of living relatives") +
-  theme_bw() +
-  facet_wrap(~kin)
-```
-
-The one-sex model implemented in `DemoKin` assumes that the given fertility input applies to both sexes. 
-Note that, if using survival rates ($S_x$) instead of probabilities ($p_x$), fertility vectors should account for female person-year exposure, using: $(\frac{f_x+f_{x+1}S_x}{2})\frac{L_0}{l_0}$ instead of only $fx$; see Preston et.al [-@preston_demography:_2001]).
-
-The `kin` function also includes a summary output with the count of living kin, mean and standard deviation of kin age, by type of kin, for each Focal's age:
-
-```{r, fig.height=6, fig.width=8}
-swe_2015[["kin_summary"]] %>% 
-  filter(age_focal == 35) %>% 
-  select(kin, count_living, mean_age, sd_age) %>% 
-  mutate_if(is.numeric, round, 2) %>% 
-  kable()
-```
-
-Finally, we can visualize the estimated kin counts by type of kin using a network diagram. Following with the age 35:
-
-```{r, fig.height=6, fig.width=8, dpi=900, message=FALSE, warning=FALSE}
-swe_2015[["kin_summary"]] %>% 
-  filter(age_focal == 35) %>% 
-  select(kin, count = count_living) %>% 
-  plot_diagram(rounding = 2)
-```
-
-
-## 2. Kin counts with time-variant rates
-
-The demography of Sweden is, in reality, changing every year. This means that Focal and her relatives will have experienced changing mortality and fertility rates over time.
-We account for this, by using the time-variant models introduced by Caswell and Song [-@caswell_formal_2021]. 
-Let's take a look at the resulting kin counts for a Focal born in 1960, limiting the output to the relative types given in the argument `output_kin`:
-
-```{r, fig.height=6, fig.width=8}
-swe_time_varying <- 
-  kin(
-    p = swe_px,
-    f = swe_asfr,
-    n = swe_pop,
-    time_invariant =FALSE,
-    output_cohort = 1960,
-    output_kin = c("d","gd","ggd","m","gm","ggm")
-    )
-
-swe_time_varying$kin_summary %>%
-  ggplot(aes(age_focal,count_living,color=factor(cohort))) +
-  scale_y_continuous(name = "",labels = seq(0,3,.2),breaks = seq(0,3,.2))+
-  geom_line(color = 1)+
-  geom_vline(xintercept = 35, color=2)+
-  labs(y = "Expected number of living relatives") +
-  facet_wrap(~kin,scales = "free")+
-  theme_bw()
-
-```
-
-<!-- As you see the estimates stop at age 55, because no data is after 2015. -->
-
-## 3. Kin deaths
-
-Kin loss can have severe consequences for bereaved relatives. It can also affect the provision of care support and intergenerational transfers over the life course. 
-The function `kin` also includes information on the number of relatives lost by Focal during her life, stored in the column `count_cum_death`:
-
-```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE}
-swe_time_varying$kin_summary %>%
-  ggplot() +
-  geom_line(aes(age_focal, count_cum_dead)) +
-  labs(y = "Expected number of deceased relatives") +
-  theme_bw() +
-  facet_wrap(~kin,scales="free")
-```
-
-Given these population-level measures, we can compute Focal's the mean age at the time of her relative's death. For a Focal aged 50 yo:
-
-```{r}
-swe_time_varying$kin_summary %>% 
-  filter(age_focal == 50) %>% 
-  select(kin,count_cum_dead,mean_age_lost) %>% 
-  mutate_if(is.numeric, round, 2) %>% 
-  kable()
-```
-
-## 4. Prevalences
-
-Given the distribution of kin by age, we can compute the expected portion of living kin in some stage given a set of prevalences by age (e.g., a disease, employment, etc.). This is known as the Sullivan Method in the life-table literature. A matrix formulation for same results can be found in Caswell [-@caswell_formal_2019], which can also be extended to a time-variant framework. 
-
-```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
-# let´s create some prevalence by age
-swe_2015_prevalence <- 
-  tibble(
-    age_kin = unique(swe_2015$kin_full$age_kin),
-    prev = .005 * exp(.05 * age_kin)
-    )
-# join to kin count estimates and plot
-swe_2015$kin_full %>% 
-  left_join(swe_2015_prevalence) %>% 
-  group_by(kin, age_focal) %>% 
-  summarise(
-    prevalent = sum(living * prev),
-    no_prevalent = sum(living * (1-prev))
-    ) %>% 
-  pivot_longer(cols = prevalent:no_prevalent, names_to = "prevalence_state", values_to = "count") %>% 
-  ggplot(aes(x=age_focal, y = count)) + 
-  geom_area(aes(fill=prevalence_state)) +
-  facet_wrap(~kin) +
-  theme_bw()
-
-```
-
-## 5. Multi-state models
-
-`DemoKin` allows the computation of kin structures in a multi-state framework, classifying individuals jointly by age and some other feature (e.g., stages of a disease). For this, we need mortality and fertility data for each possible stage and probabilities of changing state by age.
-
-Let's consider the example of Slovakia given by Caswell [-@caswell_formal_2021], where stages are parity states. 
-`DemoKin` includes the data to replicate this analysis for the year 1980:
-
-- The data.frame `svk_fxs` is the fertility rate by age (rows) for each parity stage (columns). The first stage represents $parity=0$; the second stage, $parity=1$; and so on, until finally the sixth stage represents $parity\geq5$.
-- The data.frame `svk_Hxs` has a similar structure but with $1$'s in the ages corresponding to newborns (the first age in our example). 
-- The data.frame `svk_pxs` has the same structure and represents survival probabilities. 
-- The list `svk_Uxs` has the same number of elements and ages (in this case 110, where $omega$ is 109). For each age, it contains a column-stochastic transition matrix with dimension for the state space. The entries are transition probabilities conditional on survival. 
-
-Following Caswell [-@caswell_formal_2020], we can obtain the joint age-parity kin structure:
-
-```{r}
-# use birth_female=1 because fertility is for female only
-demokin_svk1980_caswell2020 <- 
-  kin_multi_stage(
-    U = svk_Uxs,
-    f = svk_fxs,
-    D = svk_pxs,
-    H = svk_Hxs, 
-    birth_female=1,
-    parity = TRUE)
-```
-
-Note that the function ask for risks already in a certain matrix format. As an example, consider the age-parity distribution of aunts, when Focal is 20 and 60 yo (this is equivalent to Figure 4 in Caswell [-@caswell_formal_2021]). 
-
-```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
-demokin_svk1980_caswell2020 %>% 
-  filter(kin %in% c("oa","ya"), age_focal %in% c(20,60)) %>% 
-  mutate(parity = as.integer(stage_kin)-1,
-         parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity))
-         ) %>% 
-  group_by(age_focal, age_kin, parity) %>% 
-  summarise(count= sum(living)) %>% 
-  ggplot() +
-  geom_bar(aes(x=age_kin, y = count, fill=parity), stat = "identity") +
-  geom_vline(aes(xintercept = age_focal), col=2) +
-  labs(y = "Number of aunts") +
-  theme_bw() +
-  facet_wrap(~age_focal, nrow = 2)
-```
-
-We can also see the portion of living daughters and mothers at different parity stages over Focal's life-course (this is equivalent to Figure 9 and 10 in Caswell [-@caswell_formal_2021]). 
-
-```{r, message=FALSE, warning=FALSE, fig.height=6, fig.width=10}
-demokin_svk1980_caswell2020 %>% 
-  filter(kin %in% c("d","m")) %>% 
-  mutate(parity = as.integer(stage_kin)-1,
-         parity = case_when(parity == 5 ~ "5+", T ~ as.character(parity))) %>% 
-  group_by(age_focal, kin, parity) %>% 
-  summarise(count= sum(living)) %>% 
-  DemoKin::rename_kin() %>% 
-  ggplot() +
-  geom_bar(aes(x=age_focal, y = count, fill=parity), stat = "identity") +
-  labs(y = "Kin count") +
-  theme_bw() +
-  facet_wrap(~kin, nrow = 2)
-```
-
-This function `kin_multi_stage` can be generalized to any kind of state (be sure to set parameter `parity = FALSE`, de default).
-
-## References
diff --git a/vignettes/Reference_TwoSex.Rmd b/vignettes/Reference_TwoSex.Rmd
deleted file mode 100644
index 0c0064b..0000000
--- a/vignettes/Reference_TwoSex.Rmd
+++ /dev/null
@@ -1,329 +0,0 @@
----
-title: "Two-sex kinship model"
-bibliography: references.bib
-output: 
-  html_document:
-    toc: true
-    toc_depth: 1
-vignette: >
-  %\VignetteIndexEntry{Reference_TwoSex}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, eval = T, include=FALSE}
-knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
-# library(devtools); load_all()
-```
-
-Human males generally live shorter and reproduce later than females. 
-These sex-specific processes affect kinship dynamics in a number of ways. 
-For example, the degree to which an average member of the population, call her Focal, has a living grandparent is affected by differential mortality affecting the parental generation at older ages. 
-We may also be interested in considering how kinship structures vary by Focal's sex: a male Focal may have a different number of grandchildren than a female Focal given differences in fertility by sex. 
-Documenting these differences matters since women often face greater expectations to provide support and informal care to relatives.
-As they live longer, they may find themselves at greater risk of being having no living kin.
-The function `kin2sex` implements two-sex kinship models as introduced by Caswell [-@caswell_formal_2022]. 
-This vignette show how to run two-sex models and highlights some of the advantages of this model over one-sex models in populations with time-invariant and time-variant rates.
-
-```{r, message=FALSE, warning=FALSE}
-# library(DemoKin)
-library(tidyr)
-library(dplyr)
-library(ggplot2)
-library(knitr)
-pkgload::load_all()
-# devtools::load_all()
-```
-
-### 1. Demographic rates by sex
-
-Data on female fertility by age is less common than female fertility. Schoumaker (2019) shows that male TFR is almost always higher than female Total Fertility Rates (TFR) using a sample of 160 countries. 
-For this example, we use data from 2012 France to exemplify the use of the two-sex function. 
-Data on female and male fertility and mortality are included in `DemoKin`. In this population, male and female TFR is almost identical (1.98 and 1.99) but the distributions of fertility by sex varies over age:
-
-```{r}
-data(fra_asfr_sex, package = "DemoKin")
-data(fra_surv_sex, package = "DemoKin")
-fra_fert_f <- fra_asfr_sex[,"ff"]
-fra_fert_m <- fra_asfr_sex[,"fm"]
-fra_surv_f <- fra_surv_sex[,"pf"]
-fra_surv_m <- fra_surv_sex[,"pm"]
-
-sum(fra_fert_m)-sum(fra_fert_f)
-
-data.frame(value = c(fra_fert_f, fra_fert_m, fra_surv_f, fra_surv_m),
-           age = rep(0:100, 4),
-           sex = rep(c(rep("f", 101), rep("m", 101)), 2),
-           risk = c(rep("fertility rate", 101 * 2), rep("survival probability", 101 * 2))) %>%
-  ggplot(aes(age, value, col=sex)) + 
-  geom_line() + 
-  facet_wrap(~ risk, scales = "free_y") + 
-  theme_bw()
-```
-
-### 2. Time-invariant two-sex kinship models
-
-We now introduce the functions `kin2sex`, which is similar to the one-sex function `kin` (see `?kin`) with two exceptions. 
-First, the user needs to specify mortality and fertility by sex. 
-Second, the user must indicate the sex of Focal (which was assumed to be female in the one-sex model).
-Let us first consider the application for time-invariant populations:
-
-```{r}
-kin_result <- kin2sex(
-  pf = fra_surv_f,
-  pm = fra_surv_m, 
-  ff = fra_fert_f, 
-  fm = fra_fert_m, 
-  time_invariant = TRUE,
-  sex_focal = "f", 
-  birth_female = .5
-  )
-```
-
-The output of `kin2sex` is equivalent to that of `kin`, except that it includes a column `sex_kin` to specify the sex of the given relatives. 
-
-Let's group aunts and siblings to visualize the number of living kin by Focal's age.
-
-```{r, message=FALSE, warning=FALSE}
-kin_out <- kin_result$kin_summary %>% 
-  mutate(kin = case_when(kin %in% c("s", "s") ~ "s",
-                         kin %in% c("ya", "oa") ~ "a",
-                         T ~ kin)) %>%
-  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a"))
-
-kin_out %>% 
-  group_by(kin, age_focal, sex_kin) %>%
-  summarise(count=sum(count_living)) %>%
-  ggplot(aes(age_focal, count, fill=sex_kin))+
-  geom_area()+
-  theme_bw() +
-  facet_wrap(~kin)
-```
-
-**A note on terminology**
-
-The function `kin2sex` uses the same codes as `kin` to identify relatives (see `demokin_codes()`).
-Note that when running a two-sex model, the code 'm' refers to either mothers or fathers! 
-Use the column `sex_kin` to determine the sex of a given relatives.
-For example, in order to consider only sons and ignore daughters, use:
-
-```{r}
-kin_result$kin_summary %>% 
-  filter(kin == "d", sex_kin == "m") %>% 
-  head()
-```
-
-Information on kin availability by sex allows us to consider sex ratios, a traditional measure in demography, with females often in denominator. The following figure, for example, shows that a 25yo French woman in our hypothetical population can expect to have 0.5 grandfathers for every grandmother:  
-
-```{r, message=FALSE, warning=FALSE}
-kin_out %>% 
-  group_by(kin, age_focal) %>%
-  summarise(sex_ratio=sum(count_living[sex_kin=="m"], na.rm=T)/sum(count_living[sex_kin=="f"], na.rm=T)) %>%
-  ggplot(aes(age_focal, sex_ratio))+
-  geom_line()+
-  theme_bw() +
-  facet_wrap(~kin, scales = "free")
-```
-
-The experience of kin loss for Focal depends on differences in mortality between sexes. 
-A female Focal starts losing fathers earlier than mothers. 
-We see a slightly different pattern for grandparents since Focal's experience of grandparental loss is dependent on the initial availability of grandparents (i.e. if Focal's grandparent died before her birth, she will never experience his death).
-
-```{r, message=FALSE, warning=FALSE}
-# sex ratio
-kin_out %>%
-  group_by(kin, sex_kin, age_focal) %>%
-  summarise(count=sum(count_dead)) %>%
-  ggplot(aes(age_focal, count, col=sex_kin))+
-  geom_line()+
-  theme_bw() +
-  facet_wrap(~kin)
-```
-
-
-### 3. Time-variant two-sex kinship models
-
-We look at populations where demographic rates are not static but change on a yearly basis. 
-For this, we consider the case of Sweden using data pre-loaded in `DemoKin`.
-For this example, we will create 'pretend' male fertility rates by slightly perturbing the existing female rates. 
-This is a toy example, since a real two-sex model should use actual female and male rates as inputs. 
-
-```{r}
-years <- ncol(swe_px)
-ages <- nrow(swe_px)
-swe_surv_f_matrix <- swe_px
-swe_surv_m_matrix <- swe_px ^ 1.5 # artificial perturbation for this example
-swe_fert_f_matrix <- swe_asfr
-swe_fert_m_matrix <- rbind(matrix(0, 5, years),  
-            swe_asfr[-((ages-4):ages),]) * 1.05 # artificial perturbation for this example
-```
-
-This is how it looks for year 1900:
-```{r}
-bind_rows(
-  data.frame(age = 0:100, sex = "Female", component = "Fertility rate", value = swe_fert_f_matrix[,"1900"]),
-  data.frame(age = 0:100, sex = "Male", component = "Fertility rate", value = swe_fert_m_matrix[,"1900"]),
-  data.frame(age = 0:100, sex = "Female", component = "Survival probability", value = swe_surv_f_matrix[,"1900"]),
-  data.frame(age = 0:100, sex = "Male", component = "Survival probability", value = swe_surv_m_matrix[,"1900"])) %>% 
-  ggplot(aes(age, value, col = sex)) +
-  geom_line() +
-  theme_bw() +
-  facet_wrap(~component, scales = "free")
-```
-
-
-We now run the time-variant two-sex models (note the `time_invariant = FALSE` argument):
-
-```{r}
-kin_out_time_variant <- kin2sex(
-                      pf = swe_surv_f_matrix, 
-                      pm = swe_surv_m_matrix,
-                      ff = swe_fert_f_matrix, 
-                      fm = swe_fert_m_matrix,
-                      sex_focal = "f",
-                      time_invariant = FALSE,
-                      birth_female = .5,
-                      output_cohort = 1900
-                      )
-```
-
-We can plot data on kin availability alongside values coming from a time-invariant model to show how demographic change matters: the time-variant models take into account changes derived from the demographic transition, whereas the time-invariant models assume never-changing rates. 
-
-```{r, message=FALSE, warning=FALSE}
-kin_out_time_invariant <- kin2sex(
-                      swe_surv_f_matrix[,"1900"], swe_surv_m_matrix[,"1900"], 
-                      swe_fert_f_matrix[,"1900"], swe_fert_m_matrix[,"1900"], 
-                      sex_focal = "f", birth_female = .5)
-
-
-kin_out_time_variant$kin_summary %>%
-  filter(cohort == 1900) %>% mutate(type = "variant") %>%
-  bind_rows(kin_out_time_invariant$kin_summary %>% mutate(type = "invariant")) %>% 
-  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
-                         kin %in% c("ya", "oa") ~ "a",
-                         T ~ kin)) %>%
-  filter(kin %in% c("d", "m", "gm", "ggm", "s", "a")) %>%
-  group_by(type, kin, age_focal, sex_kin) %>%
-  summarise(count=sum(count_living)) %>%
-  ggplot(aes(age_focal, count, linetype=type))+
-  geom_line()+ theme_bw() +
-  facet_grid(cols = vars(kin), rows=vars(sex_kin), scales = "free")
-```
-
-### 4. Approximations
-
-Caswell [-@caswell_formal_2022] introduced two approaches for approximating two-sex kinship structures for cases when male demographic rates are not available. 
-The first is the *androgynous* approximation, which assumes equal fertility and survival for males and females.
-The second is the use of *GKP factors* apply to each type of relative (e.g., multiplying mothers by two to obtain the number of mothers and fathers). 
-
-Here, we present a visual evaluation of the accuracy of these approximations by comparing to 'true' two-sex models using the French data included with `DemoKin` for time-invariant models [@caswell_formal_2022]. 
-We start by considering the androgynous approximation.
-We compare expected kin counts by age and find high levels of consistency for all kin types, except for grandfathers and great-grandfathers:
-
-```{r, message=FALSE, warning=FALSE}
-kin_out <- kin2sex(fra_surv_f, fra_surv_m, fra_fert_f, fra_fert_m, sex_focal = "f", birth_female = .5)
-
-kin_out_androgynous <- kin2sex(fra_surv_f, fra_surv_f, fra_fert_f, fra_fert_f, sex_focal = "f", birth_female = .5)
-
-bind_rows(
-  kin_out$kin_summary %>% mutate(type = "full"),
-  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous")) %>%
-  group_by(kin, age_focal, sex_kin, type) %>%
-  summarise(count = sum(count_living)) %>%
-  ggplot(aes(age_focal, count, linetype = type)) +
-  geom_line() +
-  theme_bw() +
-  theme(legend.position = "bottom", axis.text.x = element_blank()) +
-  facet_grid(row = vars(sex_kin), col = vars(kin), scales = "free")
-```
-
-Next, we consider the use of GKP factors and find that it also approximates relatively accurately kin counts at different ages of Focal.
-These are presented as examples only. 
-Users are invited to perform more rigorous comparisons of these approximations. 
-
-```{r, message=FALSE, warning=FALSE}
-# with gkp
-kin_out_1sex <- kin(fra_surv_f, fra_fert_f, birth_female = .5)
-
-kin_out_GKP <- kin_out_1sex$kin_summary%>%
-  mutate(count_living = case_when(kin == "m" ~ count_living * 2,
-                            kin == "gm" ~ count_living * 4,
-                            kin == "ggm" ~ count_living * 8,
-                            kin == "d" ~ count_living * 2,
-                            kin == "gd" ~ count_living * 4,
-                            kin == "ggd" ~ count_living * 4,
-                            kin == "oa" ~ count_living * 4,
-                            kin == "ya" ~ count_living * 4,
-                            kin == "os" ~ count_living * 2,
-                            kin == "ys" ~ count_living * 2,
-                            kin == "coa" ~ count_living * 8,
-                            kin == "cya" ~ count_living * 8,
-                            kin == "nos" ~ count_living * 4,
-                            kin == "nys" ~ count_living * 4))
-
-bind_rows(
-  kin_out$kin_summary %>% mutate(type = "full"),
-  kin_out_androgynous$kin_summary %>% mutate(type = "androgynous"),
-  kin_out_GKP %>% mutate(type = "gkp")) %>%
-  mutate(kin = case_when(kin %in% c("ys", "os") ~ "s",
-                          kin %in% c("ya", "oa") ~ "a",
-                          kin %in% c("coa", "cya") ~ "c",
-                          kin %in% c("nys", "nos") ~ "n",
-                          T ~ kin)) %>%
-  filter(age_focal %in% c(5, 15, 30, 60, 80)) %>%
-  group_by(kin, age_focal, type) %>%
-  summarise(count = sum(count_living)) %>%
-  ggplot(aes(type, count)) +
-  geom_bar(aes(fill=type), stat = "identity") +
-  theme_bw()+theme(axis.text.x = element_text(angle = 90), legend.position = "bottom")+
-  facet_grid(col = vars(kin), row = vars(age_focal), scales = "free")
-```
-
-### 2. Causes of death
-
-Now assume we have two causes of death (COD). For females, the risk of the first COD is half the risk of the second COD for ages greater than 50. For males, the risk of the first COD is 2/3 of the second COD for ages greater than 50. We operationalize this using two matrices with dimension 2 by 101 (number of causes by number of ages).
-
-```{r}
-Hf <- matrix(c( .5, 1), nrow = 2, ncol = length(fra_surv_f))
-Hm <- matrix(c(.33, 1), nrow = 2, ncol = length(fra_surv_f))
-Hf[,1:50] <- Hm[,1:50] <- 1
-```
-
-This is a generalization of the approach outlined by Caswell [-@Caswell2023]. In the original formulation, the inputs in matrix $H$ are the hazard rates. Here, we treat them like a relative risk factor related to the underlying probability of dying. For more details, see section 2.3 and formula 30 in section A.1 of Caswell [-@Caswell2023]. Now we run the time-invariant two-sex model by COD for France 2012, assuming a death count distribution based on the two competing causes; note that the `kin2sex` function now takes the arguments `Hf` and `Hm` but the other arguments remain unchanged:
-
-```{r}
-kin_out_cod_invariant <- kin2sex(
-                      pf = fra_surv_f,
-                      pm = fra_surv_m, 
-                      ff = fra_fert_f, 
-                      fm = fra_fert_m,
-                      Hf = Hf,
-                      Hm = Hm,
-                      time_invariant = TRUE)
-```
-
-The output of `kin2sex` is the the `kin_full` data frame that we have encountered before. The only differences is that `kin_full` now includes one column for each COD specified in the input. Therefore, the number of columns will vary depending on how many COD you are considering!
-
-```{r}
-head(kin_out_cod_invariant)
-```
-
-We can now plot the death distribution by age and COD of Focal's parents when Focal is 30 yo.
-
-```{r}
-kin_out_cod_invariant %>% 
-  filter(kin == "m", age_focal == 30) %>% 
-  summarise(deadcause1 = sum(deadcause1),
-            deadcause2 = sum(deadcause2), .by = c(age_kin, sex_kin)) %>%
-  pivot_longer(deadcause1:deadcause2) %>% 
-  ggplot(aes(age_kin, value, col = sex_kin, linetype = name)) +
-  geom_line() + 
-  labs(y = "Expected number of parental deaths") +
-  theme_bw()
-```
-
-In this simplified example, the parents of Focal only died after age 50. This helped highlight the relative difference between the COD for each sex. Note that the sum of the death counts by sex gives the same result as the total deaths by sex at that age in the less complex model (i.e., the one that does not consider COD, see section 2 of this guide). 
-
-You can add as many COD as you want, but keep in mind that this can be computationally intensive. For time-variant kinship models that consider COD, you must provide a list of matrices by sex ($Hf$ and $Hm$). The elements of this list should be $H$ matrices for each year (following the same order than the mortality and fertility components). 
-
-## References
diff --git a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd b/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
deleted file mode 100644
index c519820..0000000
--- a/vignettes/Reference_TwoSex_MultiState_TimeVariant.Rmd
+++ /dev/null
@@ -1,292 +0,0 @@
----
-title: "Expected kin counts by type of relative in a two-sex multi-state time-varying framework"
-output:
-  html_document:
-    toc: true
-    toc_depth: 1
-vignette: >
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, eval = T, include=FALSE}
-knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE)
-pkgload::load_all()
-```
-
-Since the inception of Caswell's [@caswell_formal_2019] proposed one-sex time-invariant age-structured matrix model 
-of kinship, there have been many extensions to the framework (many of which are documented within this package). 
-Caswell [-@caswell_formal_2021] updated the original model to incorporate time-varying vital rates, 
-Caswell [-@caswell_formal_2022] introduced two-sexes to the model, 
-and Caswell [-@caswell_formal_2020] considered a multi-stage population of kin. 
-Here, we provide an R function which combines the three aforementioned models. 
-
-In this vignette, we'll demonstrate how the function `kin_multi_stage_time_variant_2sex` computes stage-specific kinship networks 
-encompassing both sexes for an average member of a population, the sex of whom is user specified, 
-and who is subject to time-varying demographic rates. We call this individual Focal. 
-We seek the number of, age, and stage distribution of Focal's relatives, for each age of Focal's life, and as a function of the year in which Focal is born.
-
-
-```{r}
-pkgload::load_all()
-library(Matrix)
-library(tictoc)
-options(dplyr.summarise.inform = FALSE) # hide if we don't want to see summarise output (but also #lose #progress bar)
-```
-### Kin counts by parity  ###
-
-In this example we use parity as an example stage. UK data ranging from 1965 - 2022 is sourced from 
-the [Human Mortality Database](https://www.mortality.org/) 
-and [Office for National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/conceptionandfertilityrates/adhocs/1887fertilityratesbyparityenglandandwales1934to2022/). 
-Some simplifying assumptions we make due to data availability are as follows:
-
-i) Fertility rates vary with time, are distinct among parity class, but the same over sexes (the so-called ``androgynous approximation'').
-ii) Mortality rates vary with time, are distinct across sex, but are the same over parity classes (no parity-specific mortality)
-iii) The age-specific probabilities of parity-progression vary with time, but are the same over sex (androgynous approximation again)
-
-In order to implement the model, the function `kin_multi_stage_time_variant_2sex` expects the following 7 inputs of vital rates, fed in as lists:
-
-1) `U_list_females` A list of female age-and-parity specific survival probabilities over the timescale (in matrix forms).
-This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
-
-2) `U_list_males` A list of male age-and-parity specific survival probabilities over the timescale (in matrix forms).
-This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
-
-3) `F_list_females` A list of female age-and-parity specific fertility rates over the timescale (in matrix forms).
-This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
-
-4) `F_list_males` A list of male age-and-parity specific fertility rates over the timescale (in matrix forms).
-This input list has length = the timescale, and each entry represents the rates of a specific period in matrix form: stage columns, age rows.
-
-5) `T_list_females` A list of lists of female age-specific probabilities of moving up parity over the timescale (in matrix forms).
-The outer list has length = the timescale. The inner list has length = number of ages. 
-Each outer list entry is comprised of a list of matrices (stage*stage dimensional), each matrix describes age-specific probabilities of moving stage. 
-Thus for each year, we have a list of age-specific probabilities of moving from one stage to the next.
-
-6) Same as 5) but for males
-
-7) `H_list` A list of length = timescale, in which each element is a matrix which assigns the offspring of individuals in some stage to 
-the appropriate age class (age in rows and states in columns)
-
-To avoid the need for tedious calculations to put data into such format in this vignette, these lists are constructed 
-in another file and simply imported below. The code below reads in the above function input lists.
-
-```{r eval=TRUE, message=FALSE, warning=FALSE, include=TRUE}
-F_mat_fem <- Female_parity_fert_list_UK
-F_mat_male <- Female_parity_fert_list_UK
-T_mat_fem <- Parity_transfers_by_age_list_UK
-T_mat_male <- Parity_transfers_by_age_list_UK
-U_mat_fem <- Female_parity_mortality_list_UK
-U_mat_male <- Male_parity_mortality_list_UK
-H_mat <- Redistribution_by_parity_list_UK
-
-```
-
-Recap: above are lists of period-specific demographic rates, in particular comprising:
-
-U_mat_fem: list of age by stage matrices, entries give female probability of survival.
-List starting 1965 ending 2022.
-
-U_mat_male: list of age by stage matrices, entries give female probability of survival.
-List starting 1965 ending 2022.
-
-F_mat_fem: list of age by stage matrices, entries give female fert,
-List starting 1965 ending 2022.
-
-F_mat_male == F_mat_fem.
-
-T_mat_fem: list of lists of matrices: Each outer list entry is a list of matrices where each matrix gives age-specific probabilities 
-a female moves up parity (inner list has length of number of age-classes).
-Outer list starting 1965 ending 2022
-
-T_mat_male == T_mat_fem.
-
-H_mat: list of matrices which redistributes newborns to age-class 1 and parity 0. No time-variation.
-
-### 1. Accumulated number of kin Focal expects over the lifecourse under time-varying rates from 1965 to 2005 ###
-
-We feed the above inputs into the matrix model, along with other arguments:
-
--   UK sex ratio --> `birth_female` = 0.49
--   We are considering parity --> `parity` = TRUE
--   We want some of Focal's kin network --> `output_kin` = c("d", "oa", "ys", "os")
--   Accumulated kin in this example --> `summary_kin` = TRUE
--   Focal is female --> `sex_Focal` = "Female"
--   Focal born into parity 0 --> `initial_stage_Focal` = 1
--   timescale as ouptut -- > `output_years` = c(1965, 1975, 1985, 1995, 2005)
-
-Accumulated kin are outputted by the argument `summary_kin` = TRUE. In such cases, for each age of Focal, we sum over all possible ages of kin yielding the marginal stage 
-distribution of kin. 
-
-The first sets of time-varying vital rates in our input lists are e.g., U_mat_fem[[1]] (corresponding to mortality in 1965), 
-the 41-st entry is U_mat_fem[[(1+40)]] (mortality in 2005). We require consistency between the length of the list of vital rates
-and the timescale: U_mat_fem[[1:(1+40)]] = in length = seq(1965,2005). Therefore we use the input lists of demographic rates
-
-`U_list_females` = U_mat_fem[1:(1+no_years)] which runs from U_mat_fem[[1]] = 1965 set of rates, up to U_mat_fem[[41]] = 2005 set of rates, 
-and so on...
-
-> this run takes some time (round 10 min) so we don´t include the output in the vignette. Please try it!
-
-```{r, message=FALSE, warning=FALSE}
-# Run kinship model for a female Focal over a timescale of no_years (we use 40 here)
-no_years <- 40
-# and we start projecting kin in 1965
-# We decide here to count accumulated kin by age of Focal, and not distributions of kin
-kin_out_1965_2005 <-
-  kin_multi_stage_time_variant_2sex(U_list_females = U_mat_fem[1:(1+no_years)],
-                                    U_list_males = U_mat_male[1:(1+no_years)],
-                                    F_list_females = F_mat_fem[1:(1+no_years)],
-                                    F_list_males = F_mat_male[1:(1+no_years)],
-                                    T_list_females = T_mat_fem[1:(1+no_years)],
-                                    T_list_males = T_mat_fem[1:(1+no_years)],
-                                    H_list = H_mat[1:(1+no_years)],
-                                    birth_female = 1 - 0.51, ## Sex ratio -- UK value
-                                    parity = TRUE,
-                                    output_kin = c("d", "oa", "ys", "os"),
-                                    summary_kin = TRUE,
-                                    sex_Focal = "Female", ##  define Focal's sex at birth
-                                    initial_stage_Focal = 1, ## Define Focal's stage at birth
-                                    output_years = c(1965, 1975, 1985, 1995, 2005) ## the sequence of years we run the function over
-)
-
-```
-### 1.1. Visualizing the output ###
-
-```{r, message=FALSE, warning=FALSE}
-
-head(kin_out_1965_2005$kin_summary)
-```
-
-Notice the structure of the output data. We have columns `age_focal` and `kin_stage` because we sum over all ages of kin, 
-and produce the marginal stage distribution given age of Focal. We have a column corresponding to sex of kin `sex_kin`,
-a column showing which `year` we are considering, and a column headed `group` which selects the kin type.
-Finally, we have columns showing Focal's cohort of birth `cohort` (e.g., year - age of Focal), and an as.factor() equivalent.
-
-
-### 1.1.1. Plotting kin for an average Focal at some fixed period in time ###
-
-Let's suppose that we really want to understand the age*parity distributions of the accumulated number 
-of aunts and uncles older than Focal's mother and father, for each age of Focal, over years 1965, 1975, 1985, 1995, 2005. 
-Some people will do.... 
-
-We restrict Focal's kinship network to aunts and uncles older than Focal's mother by `group` == "oa". We visualise the marginal
-parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different colour schemes. Implicit in the 
-below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, 
-while a 40 year old Focal was born in 1965.
-
-```{r, fig.height=6, fig.width=8}
-kin_out_1965_2005$kin_summary %>%
-  dplyr::filter(group == "oa") %>%
-  ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
-  ggplot2::geom_bar(position = "stack", stat = "identity") +
-  ggplot2::facet_grid(sex_kin ~ year) +
-  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
-  ggplot2::theme_bw() +
-  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
-  ggplot2::ylab("Older aunts and uncles")
-```
-We could also consider any other kin in Focal's network, for instance, offspring using `group` == "d"
-
-```{r, fig.height=6, fig.width=8}
-kin_out_1965_2005$kin_summary %>%
-  dplyr::filter(group == "d") %>%
-  ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
-  ggplot2::geom_bar(position = "stack", stat = "identity") +
-  ggplot2::facet_grid(sex_kin ~ year) +
-  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
-  ggplot2::theme_bw() +
-  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
-  ggplot2::ylab("Offspring")
-```
-### 1.1.2. Plotting the kin of Focal as a function of Focal's cohort of birth ####
-
-Since we only ran the model for 40 years (between 1965-2005), there is very little scope to view kinship as cohort-specific. 
-We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring and only show Focals born of `cohort` 1910, 1925, or 1965:
-
-```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005$kin_summary %>%
-  dplyr::filter(group == "d", cohort %in% c(1910,1925,1965) ) %>%
-  ggplot2::ggplot(ggplot2::aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
-  ggplot2::geom_bar(position = "stack", stat = "identity") +
-  ggplot2::facet_grid(sex_kin ~ cohort)  +
-  ggplot2::theme_bw() +
-  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
-  ggplot2::ylab("Offspring")
-```
-The LHS plot (1910 cohort) should be interpreted as follows: if Focal is born in 1910, between 1965-2005 he/she will be 55-95 years old. Focal will have already accumulated its maximal number of offspring, and their overall number will now be dropping as mortality risk begins. The offspring of Focal will be approximately 20-35, and began if not completed reproduction/parity progression.
-
-The middle plot (1925 cohort) shows Focal between ages 40 and 80. Again, Focal will have completed reproduction and can only lose offspring as he/she ages. However, Offspring at Focal of age 40 will be around 10-20 and still have high probability of being in parity 0. Whereas, Focal at age of 80 will have offspring aged around 50, who in turn will have completed reproduction as demonstrated by 
-a well mixed parity-distribution at this age of Focal.
-
-the RHS plot (1965 cohort) simply reflects the fact that Focal will not start reproduction until around 15 years old.
-
-### 2. Now lets consider the distributions of kin Focal expects over the lifecourse ###
-
-To obtain distributions of kin as output, we simply use the `kin_full` data.frame.
-
-### 2.1. Visualizing the output ###
-
-```{r, message=FALSE, warning=FALSE}
-
-head(kin_out_1965_2005$kin_full)
-```
-
-Notice the additional column `age_kin`. Rather than grouping kin by stage and summing over all ages,
-the output here (in data frame form) gives an expected number of kin for each age*stage combination, for each age of Focal.
-
-
-### 2.1.1. Plotting kin distributions for an average Focal of fixed age, at some fixed period in time ###
-
-Lets's consider Focal is aged 50 `age_focal` == 50, and examine kin younger siblings; `group` == "ys".
-Restricting ourselves to the years 1965, 1975, 1985, 1995, 2005, we can plot the expected age*stage distribution
-of these kin over the considered periods, as shown below:
-
-```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005$kin_full %>%
-  dplyr::filter(group == "ys",
-                age_focal == 50) %>%
-  ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
-  ggplot2::geom_bar(position = "stack", stat = "identity") +
-  ggplot2::facet_grid(sex_kin ~ year) +
-  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
-  ggplot2::theme_bw() +
-  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
-  ggplot2::ylab("Younger siblings") +
-  ggplot2::ggtitle("Focal 50")
-```
-
-Notice the discontinuity along the x-abscissa at 50. This reflects the fact that Focal's younger siblings 
-cannot are of age <50. Contrastingly, when we look at the age*stage distribution of older siblings, we observe another
-discontinuity which bounds kin to be of age >50, as plotted below:
-
-```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005$kin_full %>%
-  dplyr::filter(group == "os",
-                age_focal == 50) %>%
-  ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
-  ggplot2::geom_bar(position = "stack", stat = "identity") +
-  ggplot2::facet_grid(sex_kin ~ year) +
-  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
-  ggplot2::theme_bw() +
-  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
-  ggplot2::ylab("Older siblings") +
-  ggplot2::ggtitle("Focal 50")
-```
-
-With a simple bit of playing with the output data frame, we can plot the age*stage distribution of the combined siblings of Focal
-
-```{r, fig.height = 6, fig.width = 8}
-kin_out_1965_2005$kin_full %>%
-  dplyr::filter((group == "ys" | group == "os"),
-                age_focal == 50) %>%
-  tidyr::pivot_wider(names_from = group, values_from = count) %>%
-  dplyr::mutate(count = `ys` + `os`) %>%
-  ggplot2::ggplot(ggplot2::aes(x = age_kin, y = count, color = stage_kin, fill = stage_kin)) +
-  ggplot2::geom_bar(position = "stack", stat = "identity") +
-  ggplot2::facet_grid(sex_kin ~ year) +
-  ggplot2::scale_x_continuous(breaks = c(0,10,20,30,40,50,60,70,80,90,100)) +
-  ggplot2::theme_bw() +
-  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5)) +
-  ggplot2::ylab("All siblings") +
-  ggplot2::ggtitle("Focal 50")
-```
diff --git a/vignettes/references.bib b/vignettes/references.bib
index a2fc0ff..43cee86 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -1,3 +1,11 @@
+@book{Keyfitz2005,
+  title={Applied mathematical demography},
+  author={Keyfitz, Nathan and Caswell, Hal and others},
+  volume={47},
+  year={2005},
+  publisher={Springer}
+}
+
 @article{Caswell2023,
 	author = {Caswell, Hal and Margolis, Rachel and Verdery, Ashton},
 	title = {{The formal demography of kinship V: Kin loss, bereavement, and causes of death}},

From ff59d89b597b66f07ffbc0b5d5277872d06a3135 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 16:41:14 -0300
Subject: [PATCH 73/89] fuera reads

---
 vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd b/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
index e8d4fdc..07eb591 100644
--- a/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
+++ b/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
@@ -92,7 +92,6 @@ If you haven't already installed the required packages from the previous vignett
 install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
-install.packages("readr")     # Data import
 install.packages("knitr")     # Document generation
 install.packages("data.table")# Efficient data handling
 install.packages("Matrix")    # Matrix operations
@@ -114,7 +113,6 @@ rm(list = ls())
 library(dplyr)    # For data manipulation
 library(tidyr)    # For restructuring data
 library(ggplot2)  # For visualization
-library(readr)    # For reading data
 library(knitr)    # For document generation
 library(Matrix)   # For matrix operations
 library(tictoc)   # For timing operations
@@ -184,7 +182,7 @@ These lists contain period-specific demographic rates:
 
 Now let's implement the two-sex time-varying multi-state model with parity as the stage variable:
 
-```{r parity_model, message=FALSE, warning=FALSE, eval=TRUE}
+```{r parity_model, message=FALSE, warning=FALSE, eval=FALSE}
 # Define time period and parameters
 no_years <- 40  # Run the model for 40 years (1965-2005)
 
@@ -213,7 +211,7 @@ kin_out_1965_2005 <-
 
 Now we need to recode the stage variables to show meaningful parity labels:
 
-```{r parity_output_recode, message=FALSE, warning=FALSE, eval=TRUE}
+```{r parity_output_recode, message=FALSE, warning=FALSE, eval=FALSE}
 # After running the model, recode the parity stage values
 kin_out_1965_2005$kin_summary <- 
   kin_out_1965_2005$kin_summary %>%
@@ -233,7 +231,7 @@ kin_out_1965_2005$kin_full <-
 
 Let's examine the structure of the output:
 
-```{r parity_output_show, message=FALSE, warning=FALSE, eval=TRUE}
+```{r parity_output_show, message=FALSE, warning=FALSE, eval=FALSE}
 head(kin_out_1965_2005$kin_summary)
 ```
 
@@ -253,7 +251,7 @@ Let's visualize the distribution of older aunts and uncles by parity for differe
 
 We first restrict Focal's kinship network to aunts and uncles older than Focal's mother by setting `group` == "oa". We visualize the marginal parity distributions of kin: `stage_kin`, for each age of Focal `age_focal`, using different color schemes. Implicit in the below plot is that we really plot Focal's born into different `cohort` -- i.e., in the 2005 panel we show a 50 year old Focal was born in 1955, while a 40 year old Focal was born in 1965.
 
-```{r parity_aunts_uncles, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+```{r parity_aunts_uncles, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=FALSE}
 kin_out_1965_2005$kin_summary %>%
   filter(group == "oa") %>%
   ggplot(aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
@@ -274,7 +272,7 @@ kin_out_1965_2005$kin_summary %>%
 
 We could also consider any other kin in Focal's network, for instance, offspring using `group` == "d":
 
-```{r parity_offspring, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+```{r parity_offspring, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=FALSE}
 kin_out_1965_2005$kin_summary %>%
   filter(group == "d") %>%
   ggplot(aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
@@ -299,7 +297,7 @@ Since we only ran the model for 40 years (between 1965-2005), there is very litt
 
 We can however compare cohorts for 40-year segments of Focal's life. Below, following from the above example, we once again consider offspring and only show Focals born of `cohort` 1910, 1925, or 1965:
 
-```{r parity_cohort, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+```{r parity_cohort, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=FALSE}
 kin_out_1965_2005$kin_summary %>%
   filter(group == "d", cohort %in% c(1910, 1925, 1965)) %>%
   ggplot(aes(x = age_focal, y = count, color = stage_kin, fill = stage_kin)) +
@@ -327,7 +325,7 @@ kin_out_1965_2005$kin_summary %>%
 
 For more detailed analysis, we can examine the age and parity distribution of specific relatives. Let's look at the younger siblings of a 50-year-old Focal across different years:
 
-```{r parity_siblings_young, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+```{r parity_siblings_young, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=FALSE}
 kin_out_1965_2005$kin_full %>%
   filter(group == "ys",
          age_focal == 50) %>%
@@ -351,7 +349,7 @@ Notice the discontinuity along the x-axis at age 50. This reflects the fact that
 
 With a simple manipulation of the output data frame, we can also plot the age and parity distribution of all siblings combined:
 
-```{r parity_siblings_all, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=TRUE}
+```{r parity_siblings_all, message=FALSE, warning=FALSE, fig.height=6, fig.width=8, eval=FALSE}
 kin_out_1965_2005$kin_full %>%
   filter((group == "ys" | group == "os"),
          age_focal == 50) %>%

From 56eaf10bbe9de8799710d73fdb3fb07062b00772 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 17:00:36 -0300
Subject: [PATCH 74/89] remove readr

---
 vignettes/1_1_OneSex_TimeInvariant_Age.Rmd      | 2 --
 vignettes/1_2_OneSex_TimeVarying_Age.Rmd        | 2 --
 vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd      | 2 --
 vignettes/1_4_TwoSex_TimeVarying_Age.Rmd        | 2 --
 vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd | 2 --
 5 files changed, 10 deletions(-)

diff --git a/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd b/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
index c17b222..9f45037 100644
--- a/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
+++ b/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
@@ -96,7 +96,6 @@ rm(list = ls())
 install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
-install.packages("readr")     # Data import
 install.packages("knitr")     # Document generation
 install.packages("data.table")# Efficient data handling
 install.packages("Matrix")    # Matrix operations
@@ -117,7 +116,6 @@ Let's begin by loading the necessary packages for our analysis:
 library(dplyr)    # For data manipulation
 library(tidyr)    # For restructuring data
 library(ggplot2)  # For visualization
-library(readr)    # For reading data
 library(knitr)    # For document generation
 ```
 
diff --git a/vignettes/1_2_OneSex_TimeVarying_Age.Rmd b/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
index f82a260..1f3d488 100644
--- a/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
+++ b/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
@@ -95,7 +95,6 @@ rm(list = ls())
 install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
-install.packages("readr")     # Data import
 install.packages("knitr")     # Document generation
 install.packages("data.table")# Efficient data handling
 install.packages("Matrix")    # Matrix operations
@@ -116,7 +115,6 @@ Let's load the necessary packages for our analysis:
 library(dplyr)    # For data manipulation
 library(tidyr)    # For restructuring data
 library(ggplot2)  # For visualization
-library(readr)    # For reading data
 library(knitr)    # For document generation
 ```
 
diff --git a/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd b/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
index c8211ff..fc5f08c 100644
--- a/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
+++ b/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
@@ -95,7 +95,6 @@ If you haven't already installed the required packages from the previous vignett
 install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
-install.packages("readr")     # Data import
 install.packages("knitr")     # Document generation
 install.packages("data.table")# Efficient data handling
 install.packages("Matrix")    # Matrix operations
@@ -117,7 +116,6 @@ rm(list = ls())
 library(dplyr)    # For data manipulation
 library(tidyr)    # For restructuring data
 library(ggplot2)  # For visualization
-library(readr)    # For reading data
 library(knitr)    # For document generation
 ```
 
diff --git a/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd b/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
index 90452ea..e593f40 100644
--- a/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
+++ b/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
@@ -85,7 +85,6 @@ If you haven't already installed the required packages from the previous vignett
 install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
-install.packages("readr")     # Data import
 install.packages("knitr")     # Document generation
 install.packages("data.table")# Efficient data handling
 install.packages("Matrix")    # Matrix operations
@@ -107,7 +106,6 @@ rm(list = ls())
 library(dplyr)    # For data manipulation
 library(tidyr)    # For restructuring data
 library(ggplot2)  # For visualization
-library(readr)    # For reading data
 library(knitr)    # For document generation
 ```
 
diff --git a/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd b/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
index c9828b3..e96b306 100644
--- a/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
+++ b/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
@@ -95,7 +95,6 @@ If you haven't already installed the required packages from the previous vignett
 install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
-install.packages("readr")     # Data import
 install.packages("knitr")     # Document generation
 install.packages("data.table")# Efficient data handling
 install.packages("Matrix")    # Matrix operations
@@ -117,7 +116,6 @@ rm(list = ls())
 library(dplyr)    # For data manipulation
 library(tidyr)    # For restructuring data
 library(ggplot2)  # For visualization
-library(readr)    # For reading data
 library(knitr)    # For document generation
 ```
 

From 9d8347a7be85f5c921fec49cdcbcd96c84685c79 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 24 Apr 2025 18:13:24 -0300
Subject: [PATCH 75/89] readme

---
 README.Rmd                               | 111 ++++++-----
 README.md                                | 172 ++++++++---------
 docs/index.html                          | 236 ++++++++---------------
 docs/pkgdown.yml                         |   2 +-
 man/figures/README-unnamed-chunk-3-1.png | Bin 0 -> 15508 bytes
 5 files changed, 226 insertions(+), 295 deletions(-)
 create mode 100644 man/figures/README-unnamed-chunk-3-1.png

diff --git a/README.Rmd b/README.Rmd
index a817bde..42a17f7 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -19,76 +19,79 @@ library(knitr)
 
 # DemoKin 
 
-:::::::::::::: {.columns}
-::: {.column width="60%"}
+::: {.columns}
+::: {.column width="30%"}
 
-
-`DemoKin` uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell [-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2022], and Caswell and Song [-@caswell_formal_2021]. It draws on previous theoretical development by Goodman, Keyfitz and Pullum [-@goodman_family_1974].
+<img src="man/figures/DemoKin-Logo.png" style="width:100px;" />
 
 :::
-::: {.column width="40%"}
+::: {.column width="70%"}
 
-<img src="man/figures/DemoKin-Logo.png" align="right" width="200" />
+DemoKin is an R package for the demographic analysis of kinship networks using matrix-based models.  
+It implements methods developed by Caswell and colleagues for estimating the number and age distribution of relatives under various demographic assumptions.
 
 :::
-::::::::::::::
+:::
 
-## Installation
+## Features
 
-Download the stable version [from CRAN](https://cran.r-project.org/web/packages/DemoKin/):
+- Estimate kin counts and age distributions for various types of relatives
+- Support for one-sex and two-sex models
+- Time-invariant and time-varying approaches
+- Multi-state models incorporating additional variables like parity or education
+- Visualization tools for kinship networks
 
-``` {r, eval=FALSE, include = T}
-install.packages("DemoKin")
-```
+## Installation
 
-Or you can install the development version from GitHub:
+You can install the development version of DemoKin from GitHub:
 
-``` {r, eval=FALSE}
-# install.packages("devtools")
-devtools::install_github("IvanWilli/DemoKin")
+```{r, eval = F}
+# install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)
 ```
 
 ## Usage
 
-Consider an average Swedish woman called 'Focal.' For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life; i.e., the 'time-invariant' assumption in Caswell [-@caswell_formal_2019]. 
-
-We then ask:
+Here's a basic example of how to use DemoKin:
 
-> What is the expected number of relatives of Focal over her life course?
+```{r, eval = T}
 
-Let's explore this using the Swedish data already included with `DemoKin`.
+# Run a one-sex time-invariant kinship model using Swedish data from 2015
+kin_results <- kin(
+  p = swe_px[,"2015"],        # Survival probabilities
+  f = swe_asfr[,"2015"],      # Fertility rates
+  time_invariant = TRUE       # Use time-invariant model
+)
 
-```{r, fig.height=6, fig.width=8, message=FALSE, warning=FALSE}
-library(DemoKin)
-swe_surv_2015 <- swe_px[,"2015"]
-swe_asfr_2015 <- swe_asfr[,"2015"]
-swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
+# Visualize the expected number of living relatives by age
+kin_results$kin_summary %>%
+  rename_kin() %>%
+  ggplot2::ggplot(ggplot2::aes(age_focal, count_living)) +
+  ggplot2::geom_line() +
+  ggplot2::facet_wrap(~kin_label, scales = "free_y") +
+  ggplot2::labs(
+    title = "Expected number of living relatives by age",
+    x = "Age of focal individual",
+    y = "Number of relatives"
+  )
 ```
 
-*p* is the survival probability by age from a life table and *f* are the age specific fertility ratios by age (see `?kin` for details). 
+## Documentation
 
-Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or 'Keyfitz' kinship diagram with the function `plot_diagram`:
+For detailed documentation, please visit the [DemoKin website](https://ivanwilli.github.io/DemoKin/).
 
-```{r, fig.height=6, dpi=1200,fig.width=8, message=FALSE, warning=FALSE}
-# We need to reformat the data a little bit
-kin_total <- swe_2015$kin_summary
-# Keep only data for Focal's age 35
-kin_total <- kin_total[kin_total$age_focal == 35 , c("kin", "count_living")]
-names(kin_total) <- c("kin", "count")
-plot_diagram(kin_total, rounding = 2)
-```
+The site includes several vignettes demonstrating different types of kinship models:
 
-Relatives are identified by a unique code:
+### Models stratified by age
+- [One-sex time-invariant kinship model](https://ivanwilli.github.io/DemoKin/articles/1_1_OneSex_TimeInvariant_Age.html)
+- [One-sex time-varying kinship model](https://ivanwilli.github.io/DemoKin/articles/1_2_OneSex_TimeVarying_Age.html)
+- [Two-sex time-invariant kinship model](https://ivanwilli.github.io/DemoKin/articles/1_3_TwoSex_TimeInvariant_Age.html)
+- [Two-sex time-varying kinship model](https://ivanwilli.github.io/DemoKin/articles/1_4_TwoSex_TimeVarying_Age.html)
 
-```{r, fig.height=6, fig.width=8, echo=FALSE}
-# kable(DemoKin::demokin_codes[,c("DemoKin", "Labels_2sex")])
-kable(DemoKin::demokin_codes[,-c(2)])
-```
-
-## Vignette
-
-For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see the [Reference_OneSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html) vignette; also accessible from DemoKin: `vignette("Reference_OneSex", package = "DemoKin")`. For two-sex models, see the [Reference_TwoSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html) vignette; also accessible from DemoKin: `vignette("Reference_TwoSex", package = "DemoKin")`.
-If the vignette does not load, you may need to install the package as `devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
+### Models stratified by age and stage
+- [One-sex time-invariant multi-state model](https://ivanwilli.github.io/DemoKin/articles/2_1_OneSex_TimeInvariant_AgeStage.html)
+- [Two-sex time-varying multi-state model](https://ivanwilli.github.io/DemoKin/articles/2_2_TwoSex_TimeVarying_AgeStage.html)
 
 ## Citation
 
@@ -96,7 +99,7 @@ Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team. (2021) DemoKin:
 
 ## Acknowledgments
 
- We thank Silvia Leek from the Max Planck Institute for Demographic Research for designing the DemoKin logo. The logo includes elements that have been taken or adapted [from this file](https://commons.wikimedia.org/wiki/File:Escudo_de_la_Orden_de_San_Jer%C3%B3nimo.svg), originally by Ansunando, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0) via Wikimedia Commons. Sha Jiang provided useful comments for improving the package.
+We thank Silvia Leek from the Max Planck Institute for Demographic Research for designing the DemoKin logo. The logo includes elements that have been taken or adapted [from this file](https://commons.wikimedia.org/wiki/File:Escudo_de_la_Orden_de_San_Jer%C3%B3nimo.svg), originally by Ansunando, [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0) via Wikimedia Commons. Sha Jiang provided useful comments for improving the package.
 
 ## Get involved!
   
@@ -104,4 +107,16 @@ Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team. (2021) DemoKin:
 If you're interested in contributing, please get in touch, create an issue, or submit a pull request.
 We look forward to hearing from you!
 
-<!-- ## References -->
+## References
+
+Caswell, H. (2019). The formal demography of kinship: A matrix formulation. Demographic Research, 41, 679-712.
+
+Caswell, H. (2020). The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research, 42, 1097-1144.
+
+Caswell, H. & Song, X. (2021). The formal demography of kinship III: Kinship dynamics with time-varying demographic rates. Demographic Research, 45, 517-546.
+
+Caswell, H. & Song, X. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359-396.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
diff --git a/README.md b/README.md
index cee8664..81f972b 100644
--- a/README.md
+++ b/README.md
@@ -5,119 +5,98 @@
 
 <div class="columns">
 
-<div class="column" width="60%">
+<div class="column" width="30%">
 
-`DemoKin` uses matrix demographic methods to compute expected (average)
-kin counts from demographic rates under a range of scenarios and
-assumptions. The package is an R-language implementation of Caswell
-\[-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2022\],
-and Caswell and Song \[-@caswell_formal_2021\]. It draws on previous
-theoretical development by Goodman, Keyfitz and Pullum
-\[-@goodman_family_1974\].
+<img src="man/figures/DemoKin-Logo.png" style="width:100px;" />
 
 </div>
 
-<div class="column" width="40%">
+<div class="column" width="70%">
 
-<img src="man/figures/DemoKin-Logo.png" align="right" width="200" />
+DemoKin is an R package for the demographic analysis of kinship networks
+using matrix-based models.  
+It implements methods developed by Caswell and colleagues for estimating
+the number and age distribution of relatives under various demographic
+assumptions.
 
 </div>
 
 </div>
 
-## Installation
+## Features
 
-Download the stable version [from
-CRAN](https://cran.r-project.org/web/packages/DemoKin/):
+- Estimate kin counts and age distributions for various types of
+  relatives
+- Support for one-sex and two-sex models
+- Time-invariant and time-varying approaches
+- Multi-state models incorporating additional variables like parity or
+  education
+- Visualization tools for kinship networks
 
-``` r
-install.packages("DemoKin")
-```
+## Installation
 
-Or you can install the development version from GitHub:
+You can install the development version of DemoKin from GitHub:
 
 ``` r
-# install.packages("devtools")
-devtools::install_github("IvanWilli/DemoKin")
+# install.packages("remotes")
+remotes::install_github("IvanWilli/DemoKin")
+library(DemoKin)
 ```
 
 ## Usage
 
-Consider an average Swedish woman called ‘Focal.’ For this exercise, we
-assume a female closed population in which everyone experiences the
-Swedish 2015 mortality and fertility rates at each age throughout their
-life; i.e., the ‘time-invariant’ assumption in Caswell
-\[-@caswell_formal_2019\].
+Here’s a basic example of how to use DemoKin:
 
-We then ask:
+``` r
 
-> What is the expected number of relatives of Focal over her life
-> course?
+# Run a one-sex time-invariant kinship model using Swedish data from 2015
+kin_results <- kin(
+  p = swe_px[,"2015"],        # Survival probabilities
+  f = swe_asfr[,"2015"],      # Fertility rates
+  time_invariant = TRUE       # Use time-invariant model
+)
+
+# Visualize the expected number of living relatives by age
+kin_results$kin_summary %>%
+  rename_kin() %>%
+  ggplot2::ggplot(ggplot2::aes(age_focal, count_living)) +
+  ggplot2::geom_line() +
+  ggplot2::facet_wrap(~kin_label, scales = "free_y") +
+  ggplot2::labs(
+    title = "Expected number of living relatives by age",
+    x = "Age of focal individual",
+    y = "Number of relatives"
+  )
+#> Joining with `by = join_by(kin)`
+```
 
-Let’s explore this using the Swedish data already included with
-`DemoKin`.
+<img src="man/figures/README-unnamed-chunk-3-1.png" width="100%" />
 
-``` r
-library(DemoKin)
-swe_surv_2015 <- swe_px[,"2015"]
-swe_asfr_2015 <- swe_asfr[,"2015"]
-swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015, time_invariant = TRUE)
-```
+## Documentation
 
-*p* is the survival probability by age from a life table and *f* are the
-age specific fertility ratios by age (see `?kin` for details).
+For detailed documentation, please visit the [DemoKin
+website](https://ivanwilli.github.io/DemoKin/).
 
-Now, we can visualize the implied kin counts (i.e., the average number
-of living kin) of Focal at age 35 using a network or ‘Keyfitz’ kinship
-diagram with the function `plot_diagram`:
+The site includes several vignettes demonstrating different types of
+kinship models:
 
-``` r
-# We need to reformat the data a little bit
-kin_total <- swe_2015$kin_summary
-# Keep only data for Focal's age 35
-kin_total <- kin_total[kin_total$age_focal == 35 , c("kin", "count_living")]
-names(kin_total) <- c("kin", "count")
-plot_diagram(kin_total, rounding = 2)
-```
+### Models stratified by age
+
+- [One-sex time-invariant kinship
+  model](https://ivanwilli.github.io/DemoKin/articles/1_1_OneSex_TimeInvariant_Age.html)
+- [One-sex time-varying kinship
+  model](https://ivanwilli.github.io/DemoKin/articles/1_2_OneSex_TimeVarying_Age.html)
+- [Two-sex time-invariant kinship
+  model](https://ivanwilli.github.io/DemoKin/articles/1_3_TwoSex_TimeInvariant_Age.html)
+- [Two-sex time-varying kinship
+  model](https://ivanwilli.github.io/DemoKin/articles/1_4_TwoSex_TimeVarying_Age.html)
 
-<img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
-
-Relatives are identified by a unique code:
-
-| DemoKin | Labels_female               | Labels_male                   | Labels_2sex                       |
-|:--------|:----------------------------|:------------------------------|:----------------------------------|
-| coa     | Cousins from older aunts    | Cousins from older uncles     | Cousins from older aunts/uncles   |
-| cya     | Cousins from younger aunts  | Cousins from younger uncles   | Cousins from younger aunts/uncles |
-| c       | Cousins                     | Cousins                       | Cousins                           |
-| d       | Daughters                   | Sons                          | Children                          |
-| gd      | Grand-daughters             | Grand-sons                    | Grand-childrens                   |
-| ggd     | Great-grand-daughters       | Great-grand-sons              | Great-grand-childrens             |
-| ggm     | Great-grandmothers          | Great-grandfathers            | Great-grandfparents               |
-| gm      | Grandmothers                | Grandfathers                  | Grandparents                      |
-| m       | Mother                      | Father                        | Parents                           |
-| nos     | Nieces from older sisters   | Nephews from older brothers   | Niblings from older siblings      |
-| nys     | Nieces from younger sisters | Nephews from younger brothers | Niblings from younger siblings    |
-| n       | Nieces                      | Nephews                       | Niblings                          |
-| oa      | Aunts older than mother     | Uncles older than fathers     | Aunts/Uncles older than parents   |
-| ya      | Aunts younger than mother   | Uncles younger than father    | Aunts/Uncles younger than parents |
-| a       | Aunts                       | Uncles                        | Aunts/Uncles                      |
-| os      | Older sisters               | Older brothers                | Older siblings                    |
-| ys      | Younger sisters             | Younger brothers              | Younger siblings                  |
-| s       | Sisters                     | Brothers                      | Siblings                          |
-
-## Vignette
-
-For more details, including an extension to time-variant rates, deceased
-kin, and multi-state models in a one-sex framework, see the
-[Reference_OneSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html)
-vignette; also accessible from DemoKin:
-`vignette("Reference_OneSex", package = "DemoKin")`. For two-sex models,
-see the
-[Reference_TwoSex](https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html)
-vignette; also accessible from DemoKin:
-`vignette("Reference_TwoSex", package = "DemoKin")`. If the vignette
-does not load, you may need to install the package as
-`devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)`.
+### Models stratified by age and stage
+
+- [One-sex time-invariant multi-state
+  model](https://ivanwilli.github.io/DemoKin/articles/2_1_OneSex_TimeInvariant_AgeStage.html)
+- [Two-sex time-varying multi-state
+  model](https://ivanwilli.github.io/DemoKin/articles/2_2_TwoSex_TimeVarying_AgeStage.html)
 
 ## Citation
 
@@ -141,4 +120,23 @@ Commons. Sha Jiang provided useful comments for improving the package.
 contributing, please get in touch, create an issue, or submit a pull
 request. We look forward to hearing from you!
 
-<!-- ## References -->
+## References
+
+Caswell, H. (2019). The formal demography of kinship: A matrix
+formulation. Demographic Research, 41, 679-712.
+
+Caswell, H. (2020). The formal demography of kinship II: Multistate
+models, parity, and sibship. Demographic Research, 42, 1097-1144.
+
+Caswell, H. & Song, X. (2021). The formal demography of kinship III:
+Kinship dynamics with time-varying demographic rates. Demographic
+Research, 45, 517-546.
+
+Caswell, H. & Song, X. (2022). The formal demography of kinship IV:
+Two-sex models and their approximations. Demographic Research, 47,
+359-396.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file
+for details.
diff --git a/docs/index.html b/docs/index.html
index 6bba705..862fa09 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -62,178 +62,84 @@
 <div class="page-header"><h1 id="demokin">DemoKin<a class="anchor" aria-label="anchor" href="#demokin"></a>
 </h1></div>
 <div class="columns">
-<div class="column" style="width:60%;">
-<p><code>DemoKin</code> uses matrix demographic methods to compute expected (average) kin counts from demographic rates under a range of scenarios and assumptions. The package is an R-language implementation of Caswell [-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2022], and Caswell and Song [-@caswell_formal_2021]. It draws on previous theoretical development by Goodman, Keyfitz and Pullum [-@goodman_family_1974].</p>
+<div class="column" style="width:30%;">
+<p><img src="reference/figures/DemoKin-Logo.png" style="width:100px;"></p>
 </div>
-<div class="column" style="width:40%;">
-<p><img src="reference/figures/DemoKin-Logo.png" align="right" width="200"></p>
+<div class="column" style="width:70%;">
+<p>DemoKin is an R package for the demographic analysis of kinship networks using matrix-based models.<br>
+It implements methods developed by Caswell and colleagues for estimating the number and age distribution of relatives under various demographic assumptions.</p>
 </div>
 </div>
 <div class="section level2">
+<h2 id="features">Features<a class="anchor" aria-label="anchor" href="#features"></a>
+</h2>
+<ul>
+<li>Estimate kin counts and age distributions for various types of relatives</li>
+<li>Support for one-sex and two-sex models</li>
+<li>Time-invariant and time-varying approaches</li>
+<li>Multi-state models incorporating additional variables like parity or education</li>
+<li>Visualization tools for kinship networks</li>
+</ul>
+</div>
+<div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
-<p>Download the stable version <a href="https://cran.r-project.org/web/packages/DemoKin/" class="external-link">from CRAN</a>:</p>
+<p>You can install the development version of DemoKin from GitHub:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"DemoKin"</span><span class="op">)</span></span></code></pre></div>
-<p>Or you can install the development version from GitHub:</p>
-<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># install.packages("devtools")</span></span>
-<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"IvanWilli/DemoKin"</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="co"># install.packages("remotes")</span></span>
+<span><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"IvanWilli/DemoKin"</span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/IvanWilli/DemoKin" class="external-link">DemoKin</a></span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a>
 </h2>
-<p>Consider an average Swedish woman called ‘Focal.’ For this exercise, we assume a female closed population in which everyone experiences the Swedish 2015 mortality and fertility rates at each age throughout their life; i.e., the ‘time-invariant’ assumption in Caswell [-@caswell_formal_2019].</p>
-<p>We then ask:</p>
-<blockquote>
-<p>What is the expected number of relatives of Focal over her life course?</p>
-</blockquote>
-<p>Let’s explore this using the Swedish data already included with <code>DemoKin</code>.</p>
-<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/IvanWilli/DemoKin" class="external-link">DemoKin</a></span><span class="op">)</span></span>
-<span><span class="va">swe_surv_2015</span> <span class="op">&lt;-</span> <span class="va">swe_px</span><span class="op">[</span>,<span class="st">"2015"</span><span class="op">]</span></span>
-<span><span class="va">swe_asfr_2015</span> <span class="op">&lt;-</span> <span class="va">swe_asfr</span><span class="op">[</span>,<span class="st">"2015"</span><span class="op">]</span></span>
-<span><span class="va">swe_2015</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/kin.html">kin</a></span><span class="op">(</span>p <span class="op">=</span> <span class="va">swe_surv_2015</span>, f <span class="op">=</span> <span class="va">swe_asfr_2015</span>, time_invariant <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<p><em>p</em> is the survival probability by age from a life table and <em>f</em> are the age specific fertility ratios by age (see <code><a href="reference/kin.html">?kin</a></code> for details).</p>
-<p>Now, we can visualize the implied kin counts (i.e., the average number of living kin) of Focal at age 35 using a network or ‘Keyfitz’ kinship diagram with the function <code>plot_diagram</code>:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># We need to reformat the data a little bit</span></span>
-<span><span class="va">kin_total</span> <span class="op">&lt;-</span> <span class="va">swe_2015</span><span class="op">$</span><span class="va">kin_summary</span></span>
-<span><span class="co"># Keep only data for Focal's age 35</span></span>
-<span><span class="va">kin_total</span> <span class="op">&lt;-</span> <span class="va">kin_total</span><span class="op">[</span><span class="va">kin_total</span><span class="op">$</span><span class="va">age_focal</span> <span class="op">==</span> <span class="fl">35</span> , <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"kin"</span>, <span class="st">"count_living"</span><span class="op">)</span><span class="op">]</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">kin_total</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"kin"</span>, <span class="st">"count"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="reference/plot_diagram.html">plot_diagram</a></span><span class="op">(</span><span class="va">kin_total</span>, rounding <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<p><img src="reference/figures/README-unnamed-chunk-5-1.png" width="100%"></p>
-<p>Relatives are identified by a unique code:</p>
-<table class="table">
-<colgroup>
-<col width="8%">
-<col width="27%">
-<col width="29%">
-<col width="33%">
-</colgroup>
-<thead><tr class="header">
-<th align="left">DemoKin</th>
-<th align="left">Labels_female</th>
-<th align="left">Labels_male</th>
-<th align="left">Labels_2sex</th>
-</tr></thead>
-<tbody>
-<tr class="odd">
-<td align="left">coa</td>
-<td align="left">Cousins from older aunts</td>
-<td align="left">Cousins from older uncles</td>
-<td align="left">Cousins from older aunts/uncles</td>
-</tr>
-<tr class="even">
-<td align="left">cya</td>
-<td align="left">Cousins from younger aunts</td>
-<td align="left">Cousins from younger uncles</td>
-<td align="left">Cousins from younger aunts/uncles</td>
-</tr>
-<tr class="odd">
-<td align="left">c</td>
-<td align="left">Cousins</td>
-<td align="left">Cousins</td>
-<td align="left">Cousins</td>
-</tr>
-<tr class="even">
-<td align="left">d</td>
-<td align="left">Daughters</td>
-<td align="left">Sons</td>
-<td align="left">Children</td>
-</tr>
-<tr class="odd">
-<td align="left">gd</td>
-<td align="left">Grand-daughters</td>
-<td align="left">Grand-sons</td>
-<td align="left">Grand-childrens</td>
-</tr>
-<tr class="even">
-<td align="left">ggd</td>
-<td align="left">Great-grand-daughters</td>
-<td align="left">Great-grand-sons</td>
-<td align="left">Great-grand-childrens</td>
-</tr>
-<tr class="odd">
-<td align="left">ggm</td>
-<td align="left">Great-grandmothers</td>
-<td align="left">Great-grandfathers</td>
-<td align="left">Great-grandfparents</td>
-</tr>
-<tr class="even">
-<td align="left">gm</td>
-<td align="left">Grandmothers</td>
-<td align="left">Grandfathers</td>
-<td align="left">Grandparents</td>
-</tr>
-<tr class="odd">
-<td align="left">m</td>
-<td align="left">Mother</td>
-<td align="left">Father</td>
-<td align="left">Parents</td>
-</tr>
-<tr class="even">
-<td align="left">nos</td>
-<td align="left">Nieces from older sisters</td>
-<td align="left">Nephews from older brothers</td>
-<td align="left">Niblings from older siblings</td>
-</tr>
-<tr class="odd">
-<td align="left">nys</td>
-<td align="left">Nieces from younger sisters</td>
-<td align="left">Nephews from younger brothers</td>
-<td align="left">Niblings from younger siblings</td>
-</tr>
-<tr class="even">
-<td align="left">n</td>
-<td align="left">Nieces</td>
-<td align="left">Nephews</td>
-<td align="left">Niblings</td>
-</tr>
-<tr class="odd">
-<td align="left">oa</td>
-<td align="left">Aunts older than mother</td>
-<td align="left">Uncles older than fathers</td>
-<td align="left">Aunts/Uncles older than parents</td>
-</tr>
-<tr class="even">
-<td align="left">ya</td>
-<td align="left">Aunts younger than mother</td>
-<td align="left">Uncles younger than father</td>
-<td align="left">Aunts/Uncles younger than parents</td>
-</tr>
-<tr class="odd">
-<td align="left">a</td>
-<td align="left">Aunts</td>
-<td align="left">Uncles</td>
-<td align="left">Aunts/Uncles</td>
-</tr>
-<tr class="even">
-<td align="left">os</td>
-<td align="left">Older sisters</td>
-<td align="left">Older brothers</td>
-<td align="left">Older siblings</td>
-</tr>
-<tr class="odd">
-<td align="left">ys</td>
-<td align="left">Younger sisters</td>
-<td align="left">Younger brothers</td>
-<td align="left">Younger siblings</td>
-</tr>
-<tr class="even">
-<td align="left">s</td>
-<td align="left">Sisters</td>
-<td align="left">Brothers</td>
-<td align="left">Siblings</td>
-</tr>
-</tbody>
-</table>
+<p>Here’s a basic example of how to use DemoKin:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="co"># Run a one-sex time-invariant kinship model using Swedish data from 2015</span></span>
+<span><span class="va">kin_results</span> <span class="op">&lt;-</span> <span class="fu"><a href="reference/kin.html">kin</a></span><span class="op">(</span></span>
+<span>  p <span class="op">=</span> <span class="va">swe_px</span><span class="op">[</span>,<span class="st">"2015"</span><span class="op">]</span>,        <span class="co"># Survival probabilities</span></span>
+<span>  f <span class="op">=</span> <span class="va">swe_asfr</span><span class="op">[</span>,<span class="st">"2015"</span><span class="op">]</span>,      <span class="co"># Fertility rates</span></span>
+<span>  time_invariant <span class="op">=</span> <span class="cn">TRUE</span>       <span class="co"># Use time-invariant model</span></span>
+<span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Visualize the expected number of living relatives by age</span></span>
+<span><span class="va">kin_results</span><span class="op">$</span><span class="va">kin_summary</span> <span class="op"><a href="reference/pipe.html">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="reference/rename_kin.html">rename_kin</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="reference/pipe.html">%&gt;%</a></span></span>
+<span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span><span class="va">age_focal</span>, <span class="va">count_living</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/facet_wrap.html" class="external-link">facet_wrap</a></span><span class="op">(</span><span class="op">~</span><span class="va">kin_label</span>, scales <span class="op">=</span> <span class="st">"free_y"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span></span>
+<span>    title <span class="op">=</span> <span class="st">"Expected number of living relatives by age"</span>,</span>
+<span>    x <span class="op">=</span> <span class="st">"Age of focal individual"</span>,</span>
+<span>    y <span class="op">=</span> <span class="st">"Number of relatives"</span></span>
+<span>  <span class="op">)</span></span>
+<span><span class="co">#&gt; Joining with `by = join_by(kin)`</span></span></code></pre></div>
+<p><img src="reference/figures/README-unnamed-chunk-3-1.png" width="100%"></p>
 </div>
 <div class="section level2">
-<h2 id="vignette">Vignette<a class="anchor" aria-label="anchor" href="#vignette"></a>
+<h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a>
 </h2>
-<p>For more details, including an extension to time-variant rates, deceased kin, and multi-state models in a one-sex framework, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_OneSex.html" class="external-link">Reference_OneSex</a> vignette; also accessible from DemoKin: <code><a href="https://ivanwilli.github.io/DemoKin/articles/Reference_OneSex.html">vignette("Reference_OneSex", package = "DemoKin")</a></code>. For two-sex models, see the <a href="https://cran.r-project.org/web/packages/DemoKin/vignettes/Reference_TwoSex.html" class="external-link">Reference_TwoSex</a> vignette; also accessible from DemoKin: <code><a href="https://ivanwilli.github.io/DemoKin/articles/Reference_TwoSex.html">vignette("Reference_TwoSex", package = "DemoKin")</a></code>. If the vignette does not load, you may need to install the package as <code>devtools::install_github("IvanWilli/DemoKin", build_vignettes = T)</code>.</p>
+<p>For detailed documentation, please visit the <a href="https://ivanwilli.github.io/DemoKin/">DemoKin website</a>.</p>
+<p>The site includes several vignettes demonstrating different types of kinship models:</p>
+<div class="section level3">
+<h3 id="models-stratified-by-age">Models stratified by age<a class="anchor" aria-label="anchor" href="#models-stratified-by-age"></a>
+</h3>
+<ul>
+<li><a href="https://ivanwilli.github.io/DemoKin/articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant kinship model</a></li>
+<li><a href="https://ivanwilli.github.io/DemoKin/articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying kinship model</a></li>
+<li><a href="https://ivanwilli.github.io/DemoKin/articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant kinship model</a></li>
+<li><a href="https://ivanwilli.github.io/DemoKin/articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying kinship model</a></li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="models-stratified-by-age-and-stage">Models stratified by age and stage<a class="anchor" aria-label="anchor" href="#models-stratified-by-age-and-stage"></a>
+</h3>
+<ul>
+<li><a href="https://ivanwilli.github.io/DemoKin/articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant multi-state model</a></li>
+<li><a href="https://ivanwilli.github.io/DemoKin/articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying multi-state model</a></li>
+</ul>
+</div>
 </div>
 <div class="section level2">
 <h2 id="citation">Citation<a class="anchor" aria-label="anchor" href="#citation"></a>
@@ -249,7 +155,19 @@ <h2 id="acknowledgments">Acknowledgments<a class="anchor" aria-label="anchor" hr
 <h2 id="get-involved">Get involved!<a class="anchor" aria-label="anchor" href="#get-involved"></a>
 </h2>
 <p><code>DemoKin</code> is under constant development. If you’re interested in contributing, please get in touch, create an issue, or submit a pull request. We look forward to hearing from you!</p>
-<!-- ## References -->
+</div>
+<div class="section level2">
+<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
+</h2>
+<p>Caswell, H. (2019). The formal demography of kinship: A matrix formulation. Demographic Research, 41, 679-712.</p>
+<p>Caswell, H. (2020). The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research, 42, 1097-1144.</p>
+<p>Caswell, H. &amp; Song, X. (2021). The formal demography of kinship III: Kinship dynamics with time-varying demographic rates. Demographic Research, 45, 517-546.</p>
+<p>Caswell, H. &amp; Song, X. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359-396.</p>
+</div>
+<div class="section level2">
+<h2 id="license">License<a class="anchor" aria-label="anchor" href="#license"></a>
+</h2>
+<p>This project is licensed under the MIT License - see the LICENSE file for details.</p>
 </div>
 </div>
   </main><aside class="col-md-3"><div class="links">
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 789ebab..4818624 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -8,7 +8,7 @@ articles:
   1_4_TwoSex_TimeVarying_Age: 1_4_TwoSex_TimeVarying_Age.html
   2_1_OneSex_TimeInvariant_AgeStage: 2_1_OneSex_TimeInvariant_AgeStage.html
   2_2_TwoSex_TimeVarying_AgeStage: 2_2_TwoSex_TimeVarying_AgeStage.html
-last_built: 2025-04-24T17:44Z
+last_built: 2025-04-24T21:00Z
 urls:
   reference: https://ivanwilli.github.io/DemoKin/reference
   article: https://ivanwilli.github.io/DemoKin/articles
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HcmV?d00001


From 0f80df5b0ecc8f7fcade8beaafafc7fdcd3530e5 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 25 Apr 2025 14:35:59 -0300
Subject: [PATCH 76/89] navbar custom

---
 _pkgdown.yml     |  45 ++++++------
 docs/index.html  | 135 ++++++++++++++++++++++------------
 docs/pkgdown.js  | 184 +++++++++++++++++------------------------------
 docs/pkgdown.yml |   2 +-
 4 files changed, 176 insertions(+), 190 deletions(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index eaa2767..9f99da5 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,22 +1,24 @@
 url: https://ivanwilli.github.io/DemoKin/
 template:
-  bootstrap: 5
-  bootswatch: flatly
-  bslib:
-    primary: "#0054AD"
-    border-radius: 0.5rem
-    btn-border-radius: 0.25rem
+  params:
+    bootswatch: simplex
+    theme: breeze-light
 
 navbar:
+  type: reverse
+  title: "DemoKin"
   structure:
-    left: [home, age_models, stage_models]
+    left: [home, tutorials, reference]
     right: [github]
   components:
     home:
+      strip_header: true
+      title: DemoKin
+      description: Demographic analysis of kinship networks using matrix-based models.
       icon: fas fa-home fa-lg
       href: index.html
-    age_models:
-      text: "Models by Age"
+    tutorials:
+      text: "Tutorials"
       menu:
       - text: "One-sex time-invariant model"
         href: articles/1_1_OneSex_TimeInvariant_Age.html
@@ -26,25 +28,20 @@ navbar:
         href: articles/1_3_TwoSex_TimeInvariant_Age.html
       - text: "Two-sex time-varying model"
         href: articles/1_4_TwoSex_TimeVarying_Age.html
-    stage_models:
-      text: "Models by Age and Stage"
-      menu:
       - text: "One-sex time-invariant model"
         href: articles/2_1_OneSex_TimeInvariant_AgeStage.html
       - text: "Two-sex time-varying model"
         href: articles/2_2_TwoSex_TimeVarying_AgeStage.html
+    reference:
+      - title: "References"
+      - subtitle: "Estimate kinship count distributions"
+        desc: Indicators to evaluate the degreee of age heaping
+        contents:
+          - starts_with("kin")
+      - subtitle: "Ploting kinship count distributions"
+        desc: Indicators to evaluate the degreee of age heaping
+      - contents:
+          - starts_with("plot")
     github:
       icon: fab fa-github fa-lg
       href: https://github.com/IvanWilli/DemoKin
-
-articles:
-  - title: "Models by Age"
-    contents:
-      - 1_1_OneSex_TimeInvariant_Age
-      - 1_2_OneSex_TimeVarying_Age
-      - 1_3_TwoSex_TimeInvariant_Age
-      - 1_4_TwoSex_TimeVarying_Age
-  - title: "Models by Age and Stage"
-    contents:
-      - 2_1_OneSex_TimeInvariant_AgeStage
-      - 2_2_TwoSex_TimeVarying_AgeStage
diff --git a/docs/index.html b/docs/index.html
index 862fa09..ea8f86b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -4,61 +4,98 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
 <title>Estimate Population Kin Distribution • DemoKin</title>
-<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
-<link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet">
-<script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
-<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
-<script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Estimate Population Kin Distribution">
-<meta name="description" content="Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.">
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/simplex/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
+<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
+<script src="pkgdown.js"></script><meta property="og:title" content="Estimate Population Kin Distribution">
 <meta property="og:description" content="Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.">
+<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]-->
 </head>
-<body>
-    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+<body data-spy="scroll" data-target="#toc">
 
 
-    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+    <div class="container template-home">
+      <header><div class="navbar navbar-reverse navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
 
-    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+<li>
+  <a href="index.html">
+    <span class="fas fa-home fa-lg"></span>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
-
-
-    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
-      <span class="navbar-toggler-icon"></span>
-    </button>
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Tutorials
 
-    <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto">
-<li class="active nav-item"><a class="nav-link" href="index.html"><span class="fa fas fa-home fa-lg"></span></a></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age">
-<li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
-    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+<li>
+      <a href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a>
+    </li>
   </ul>
 </li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage">
-<li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
-  </ul>
+<li>
+  <a></a>
 </li>
       </ul>
-<ul class="navbar-nav">
-<li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
+<ul class="nav navbar-nav navbar-right">
+<li>
+  <a href="https://github.com/IvanWilli/DemoKin" class="external-link">
+    <span class="fab fa-github fa-lg"></span>
+
+  </a>
+</li>
       </ul>
 </div>
+<!--/.nav-collapse -->
+  </div>
+<!--/.container -->
+</div>
+<!--/.navbar -->
 
 
-  </div>
-</nav><div class="container template-home">
-<div class="row">
-  <main id="main" class="col-md-9"><div class="section level1">
+
+      </header><div class="row">
+  <div class="contents col-md-9">
+<div class="section level1">
 <div class="page-header"><h1 id="demokin">DemoKin<a class="anchor" aria-label="anchor" href="#demokin"></a>
 </h1></div>
 <div class="columns">
@@ -170,7 +207,10 @@ <h2 id="license">License<a class="anchor" aria-label="anchor" href="#license"></
 <p>This project is licensed under the MIT License - see the LICENSE file for details.</p>
 </div>
 </div>
-  </main><aside class="col-md-3"><div class="links">
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <div class="links">
 <h2 data-toc-skip>Links</h2>
 <ul class="list-unstyled">
 <li><a href="https://cloud.r-project.org/package=DemoKin" class="external-link">View on CRAN</a></li>
@@ -206,24 +246,27 @@ <h2 data-toc-skip>Developers</h2>
 
 
 
-  </aside>
+  </div>
 </div>
 
 
-    <footer><div class="pkgdown-footer-left">
-  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+      <footer><div class="copyright">
+  <p></p>
+<p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown">
+  <p></p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
 </div>
 
-    </footer>
+      </footer>
 </div>
 
 
 
 
 
+
   </body>
 </html>
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index 1a99c65..6f0eee4 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -2,43 +2,83 @@
 (function($) {
   $(function() {
 
-    $('nav.navbar').headroom();
+    $('.navbar-fixed-top').headroom();
 
-    Toc.init({
-      $nav: $("#toc"),
-      $scope: $("main h2, main h3, main h4, main h5, main h6")
+    $('body').css('padding-top', $('.navbar').height() + 10);
+    $(window).resize(function(){
+      $('body').css('padding-top', $('.navbar').height() + 10);
     });
 
-    if ($('#toc').length) {
-      $('body').scrollspy({
-        target: '#toc',
-        offset: $("nav.navbar").outerHeight() + 1
-      });
+    $('[data-toggle="tooltip"]').tooltip();
+
+    var cur_path = paths(location.pathname);
+    var links = $("#navbar ul li a");
+    var max_length = -1;
+    var pos = -1;
+    for (var i = 0; i < links.length; i++) {
+      if (links[i].getAttribute("href") === "#")
+        continue;
+      // Ignore external links
+      if (links[i].host !== location.host)
+        continue;
+
+      var nav_path = paths(links[i].pathname);
+
+      var length = prefix_length(nav_path, cur_path);
+      if (length > max_length) {
+        max_length = length;
+        pos = i;
+      }
     }
 
-    // Activate popovers
-    $('[data-bs-toggle="popover"]').popover({
-      container: 'body',
-      html: true,
-      trigger: 'focus',
-      placement: "top",
-      sanitize: false,
-    });
+    // Add class to parent <li>, and enclosing <li> if in dropdown
+    if (pos >= 0) {
+      var menu_anchor = $(links[pos]);
+      menu_anchor.parent().addClass("active");
+      menu_anchor.closest("li.dropdown").addClass("active");
+    }
+  });
+
+  function paths(pathname) {
+    var pieces = pathname.split("/");
+    pieces.shift(); // always starts with /
+
+    var end = pieces[pieces.length - 1];
+    if (end === "index.html" || end === "")
+      pieces.pop();
+    return(pieces);
+  }
+
+  // Returns -1 if not found
+  function prefix_length(needle, haystack) {
+    if (needle.length > haystack.length)
+      return(-1);
 
-    $('[data-bs-toggle="tooltip"]').tooltip();
+    // Special case for length-0 haystack, since for loop won't run
+    if (haystack.length === 0) {
+      return(needle.length === 0 ? 0 : -1);
+    }
+
+    for (var i = 0; i < haystack.length; i++) {
+      if (needle[i] != haystack[i])
+        return(i);
+    }
+
+    return(haystack.length);
+  }
 
   /* Clipboard --------------------------*/
 
   function changeTooltipMessage(element, msg) {
-    var tooltipOriginalTitle=element.getAttribute('data-bs-original-title');
-    element.setAttribute('data-bs-original-title', msg);
+    var tooltipOriginalTitle=element.getAttribute('data-original-title');
+    element.setAttribute('data-original-title', msg);
     $(element).tooltip('show');
-    element.setAttribute('data-bs-original-title', tooltipOriginalTitle);
+    element.setAttribute('data-original-title', tooltipOriginalTitle);
   }
 
   if(ClipboardJS.isSupported()) {
     $(document).ready(function() {
-      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
+      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' type = 'submit' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left auto' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
 
       $("div.sourceCode").addClass("hasCopyButton");
 
@@ -49,114 +89,20 @@
       $('.btn-copy-ex').tooltip({container: 'body'});
 
       // Initialize clipboard:
-      var clipboard = new ClipboardJS('[data-clipboard-copy]', {
+      var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
         text: function(trigger) {
           return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
         }
       });
 
-      clipboard.on('success', function(e) {
+      clipboardBtnCopies.on('success', function(e) {
         changeTooltipMessage(e.trigger, 'Copied!');
         e.clearSelection();
       });
 
-      clipboard.on('error', function(e) {
+      clipboardBtnCopies.on('error', function() {
         changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
       });
-
     });
   }
-
-    /* Search marking --------------------------*/
-    var url = new URL(window.location.href);
-    var toMark = url.searchParams.get("q");
-    var mark = new Mark("main#main");
-    if (toMark) {
-      mark.mark(toMark, {
-        accuracy: {
-          value: "complementary",
-          limiters: [",", ".", ":", "/"],
-        }
-      });
-    }
-
-  /* Search --------------------------*/
-  /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */
-    // Initialise search index on focus
-  var fuse;
-  $("#search-input").focus(async function(e) {
-    if (fuse) {
-      return;
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-
-    $(e.target).addClass("loading");
-    var response = await fetch($("#search-input").data("search-index"));
-    var data = await response.json();
-
-    var options = {
-      keys: ["what", "text", "code"],
-      ignoreLocation: true,
-      threshold: 0.1,
-      includeMatches: true,
-      includeScore: true,
-    };
-    fuse = new Fuse(data, options);
-
-    $(e.target).removeClass("loading");
-  });
-
-  // Use algolia autocomplete
-  var options = {
-    autoselect: true,
-    debug: true,
-    hint: false,
-    minLength: 2,
-  };
-  var q;
-async function searchFuse(query, callback) {
-  await fuse;
-
-  var items;
-  if (!fuse) {
-    items = [];
-  } else {
-    q = query;
-    var results = fuse.search(query, { limit: 20 });
-    items = results
-      .filter((x) => x.score <= 0.75)
-      .map((x) => x.item);
-    if (items.length === 0) {
-      items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}];
-    }
-  }
-  callback(items);
-}
-  $("#search-input").autocomplete(options, [
-    {
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-      source: searchFuse,
-      templates: {
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-            return `${s.dir} >	<div class="search-details"> ${s.title}</div>`;
-          } else if (s.previous_headings == "") {
-            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.what}`;
-          } else {
-            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.previous_headings} > ${s.what}`;
-          }
-        },
-      },
-    },
-  ]).on('autocomplete:selected', function(event, s) {
-    window.location.href = s.path + "?q=" + q + "#" + s.id;
-  });
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 })(window.jQuery || window.$)
-
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diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 4818624..712db21 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -8,7 +8,7 @@ articles:
   1_4_TwoSex_TimeVarying_Age: 1_4_TwoSex_TimeVarying_Age.html
   2_1_OneSex_TimeInvariant_AgeStage: 2_1_OneSex_TimeInvariant_AgeStage.html
   2_2_TwoSex_TimeVarying_AgeStage: 2_2_TwoSex_TimeVarying_AgeStage.html
-last_built: 2025-04-24T21:00Z
+last_built: 2025-04-25T17:34Z
 urls:
   reference: https://ivanwilli.github.io/DemoKin/reference
   article: https://ivanwilli.github.io/DemoKin/articles

From b52577de237a7ec6a405f947af8b6e3ddc9dfee4 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 25 Apr 2025 16:24:07 -0300
Subject: [PATCH 77/89] custom ref

---
 DESCRIPTION            |   6 +-
 README.Rmd             |   4 +-
 README.md              |   9 +--
 _pkgdown.yml           |  29 ++++----
 docs/404.html          | 142 +++++++++++++++++++++++++-------------
 docs/LICENSE-text.html | 121 ++++++++++++++++++++++-----------
 docs/LICENSE.html      | 121 ++++++++++++++++++++++-----------
 docs/authors.html      | 150 +++++++++++++++++++++++++++--------------
 docs/index.html        |  12 ++--
 docs/pkgdown.yml       |   2 +-
 10 files changed, 386 insertions(+), 210 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 250613d..561a3d1 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -5,10 +5,12 @@ Description: Estimate population kin counts and its distribution by type, age an
     with time varying rates or not, and multi-stage model.
 Version: 1.0.3
 Authors@R: c(
-    person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = "cre"),
+    person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = c("aut", "cre")),
     person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"),
     person("Xi", "Song", email = "xisong@sas.upenn.edu", role = "ctb"),
-    person("Caswell", "Hal", email = "caswell@demogr.mpg.de", role = "ctb"))
+    person("Caswell", "Hal", email = "caswell@demogr.mpg.de", role = "ctb"),
+    person("Benjamin", "Schlüter", email = "benjamin.schluter@utoronto.ca", role = "ctb"),
+    person("Butterick", "Joe", email = "J.Butterick@soton.ac.uk", role = "ctb"))
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
diff --git a/README.Rmd b/README.Rmd
index 42a17f7..9c681a3 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -28,7 +28,7 @@ library(knitr)
 ::: {.column width="70%"}
 
 DemoKin is an R package for the demographic analysis of kinship networks using matrix-based models.  
-It implements methods developed by Caswell and colleagues for estimating the number and age distribution of relatives under various demographic assumptions.
+It implements methods developed by Caswell (2019) and colleagues (\ref{}) for estimating the number and age distribution of relatives under various demographic assumptions.
 
 :::
 :::
@@ -55,7 +55,7 @@ library(DemoKin)
 
 Here's a basic example of how to use DemoKin:
 
-```{r, eval = T}
+```{r, eval = F}
 
 # Run a one-sex time-invariant kinship model using Swedish data from 2015
 kin_results <- kin(
diff --git a/README.md b/README.md
index 81f972b..81d9e1b 100644
--- a/README.md
+++ b/README.md
@@ -15,9 +15,9 @@
 
 DemoKin is an R package for the demographic analysis of kinship networks
 using matrix-based models.  
-It implements methods developed by Caswell and colleagues for estimating
-the number and age distribution of relatives under various demographic
-assumptions.
+It implements methods developed by Caswell (2019) and colleagues () for
+estimating the number and age distribution of relatives under various
+demographic assumptions.
 
 </div>
 
@@ -67,11 +67,8 @@ kin_results$kin_summary %>%
     x = "Age of focal individual",
     y = "Number of relatives"
   )
-#> Joining with `by = join_by(kin)`
 ```
 
-<img src="man/figures/README-unnamed-chunk-3-1.png" width="100%" />
-
 ## Documentation
 
 For detailed documentation, please visit the [DemoKin
diff --git a/_pkgdown.yml b/_pkgdown.yml
index 9f99da5..c5d9973 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,11 +1,11 @@
 url: https://ivanwilli.github.io/DemoKin/
 template:
   params:
-    bootswatch: simplex
+    bootstrap: 5
+    bootswatch: minty
     theme: breeze-light
 
 navbar:
-  type: reverse
   title: "DemoKin"
   structure:
     left: [home, tutorials, reference]
@@ -28,20 +28,23 @@ navbar:
         href: articles/1_3_TwoSex_TimeInvariant_Age.html
       - text: "Two-sex time-varying model"
         href: articles/1_4_TwoSex_TimeVarying_Age.html
-      - text: "One-sex time-invariant model"
+      - text: "One-sex time-invariant Age-Stage model"
         href: articles/2_1_OneSex_TimeInvariant_AgeStage.html
-      - text: "Two-sex time-varying model"
+      - text: "Two-sex time-varying Age-Stage model"
         href: articles/2_2_TwoSex_TimeVarying_AgeStage.html
     reference:
-      - title: "References"
-      - subtitle: "Estimate kinship count distributions"
-        desc: Indicators to evaluate the degreee of age heaping
-        contents:
-          - starts_with("kin")
-      - subtitle: "Ploting kinship count distributions"
-        desc: Indicators to evaluate the degreee of age heaping
-      - contents:
-          - starts_with("plot")
+      text: "Functions"
+      href: reference/index.html
     github:
       icon: fab fa-github fa-lg
       href: https://github.com/IvanWilli/DemoKin
+
+# reference:
+#   - title: "Model estimation"
+#     desc: "Estimate kinship count distribution"
+#     contents:
+#       - starts_with("kin")
+#   - title: "Kinship diagram"
+#     desc: "Visualize a kinship diagram"
+#     contents:
+#       - starts_with("plot")
diff --git a/docs/404.html b/docs/404.html
index 9a326e9..d9a2111 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -4,84 +4,130 @@
 <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
-<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
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+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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+<script src="https://ivanwilli.github.io/DemoKin/bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="https://ivanwilli.github.io/DemoKin/pkgdown.css" rel="stylesheet">
+<script src="https://ivanwilli.github.io/DemoKin/pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
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+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]-->
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+<body data-spy="scroll" data-target="#toc">
+
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+    <div class="container template-title-body">
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+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="https://ivanwilli.github.io/DemoKin/index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
 
-    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-    </button>
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+<li>
+  <a href="https://ivanwilli.github.io/DemoKin/index.html">
+    <span class="fas fa-home fa-lg"></span>
 
-    <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto">
-<li class="nav-item"><a class="nav-link" href="https://ivanwilli.github.io/DemoKin/index.html"><span class="fa fas fa-home fa-lg"></span></a></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age">
-<li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
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-    <li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
-  </ul>
+  </a>
 </li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage">
-<li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="https://ivanwilli.github.io/DemoKin/articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
+<li class="dropdown">
+  <a href="https://ivanwilli.github.io/DemoKin/#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Tutorials
+
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+<li>
+      <a href="https://ivanwilli.github.io/DemoKin/articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="https://ivanwilli.github.io/DemoKin/articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="https://ivanwilli.github.io/DemoKin/articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="https://ivanwilli.github.io/DemoKin/articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="https://ivanwilli.github.io/DemoKin/articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant Age-Stage model</a>
+    </li>
+    <li>
+      <a href="https://ivanwilli.github.io/DemoKin/articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying Age-Stage model</a>
+    </li>
   </ul>
+</li>
+<li>
+  <a href="https://ivanwilli.github.io/DemoKin/reference/index.html">Functions</a>
 </li>
       </ul>
-<ul class="navbar-nav">
-<li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
+<ul class="nav navbar-nav navbar-right">
+<li>
+  <a href="https://github.com/IvanWilli/DemoKin" class="external-link">
+    <span class="fab fa-github fa-lg"></span>
+
+  </a>
+</li>
       </ul>
 </div>
+<!--/.nav-collapse -->
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+<!--/.container -->
+</div>
+<!--/.navbar -->
 
 
-  </div>
-</nav><div class="container template-title-body">
-<div class="row">
-  <main id="main" class="col-md-9"><div class="page-header">
 
+      </header><div class="row">
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 Content not found. Please use links in the navbar.
 
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+  </div>
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+</div>
+
 </div>
 
 
-    <footer><div class="pkgdown-footer-left">
-  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+
+      <footer><div class="copyright">
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+<p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown">
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+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
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diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 606ba6b..19092ed 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -1,62 +1,103 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • DemoKin</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"></head><body>
-    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-
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-    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
-
-    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
-
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
-
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-    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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-    </button>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>License • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="License"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body data-spy="scroll" data-target="#toc">
+
+
+    <div class="container template-title-body">
+      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
 
-    <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="index.html"><span class="fa fas fa-home fa-lg"></span></a></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age"><li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
-    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav"><li>
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+
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+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Tutorials
+
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu"><li>
+      <a href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant Age-Stage model</a>
+    </li>
+    <li>
+      <a href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying Age-Stage model</a>
+    </li>
   </ul></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage"><li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
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+<li>
+  <a href="reference/index.html">Functions</a>
+</li>
+      </ul><ul class="nav navbar-nav navbar-right"><li>
+  <a href="https://github.com/IvanWilli/DemoKin" class="external-link">
+    <span class="fab fa-github fa-lg"></span>
 
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+</li>
+      </ul></div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
 
-  </div>
-</nav><div class="container template-title-body">
-<div class="row">
-  <main id="main" class="col-md-9"><div class="page-header">
 
-      <h1>License</h1>
 
+      </header><div class="row">
+  <div class="contents col-md-9">
+    <div class="page-header">
+      <h1>License</h1>
     </div>
 
 <pre>YEAR: 2021
 COPYRIGHT HOLDER: DemoKin authors
 </pre>
 
-  </main></div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
+    </nav></div>
+
+</div>
 
 
-    <footer><div class="pkgdown-footer-left">
-  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+
+      <footer><div class="copyright">
+  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
 </div>
 
-    </footer></div>
+      </footer></div>
+
 
 
 
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index 102b7d2..75d8d27 100644
--- a/docs/LICENSE.html
+++ b/docs/LICENSE.html
@@ -1,44 +1,77 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>MIT License • DemoKin</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="MIT License"></head><body>
-    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-
-
-    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
-
-    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
-
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
-
-
-    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
-      <span class="navbar-toggler-icon"></span>
-    </button>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>MIT License • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="MIT License"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body data-spy="scroll" data-target="#toc">
+
+
+    <div class="container template-title-body">
+      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
 
-    <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="index.html"><span class="fa fas fa-home fa-lg"></span></a></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age"><li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
-    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
-  </ul></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage"><li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav"><li>
+  <a href="index.html">
+    <span class="fas fa-home fa-lg"></span>
+
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Tutorials
+
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu"><li>
+      <a href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant Age-Stage model</a>
+    </li>
+    <li>
+      <a href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying Age-Stage model</a>
+    </li>
   </ul></li>
-      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
-      </ul></div>
+<li>
+  <a href="reference/index.html">Functions</a>
+</li>
+      </ul><ul class="nav navbar-nav navbar-right"><li>
+  <a href="https://github.com/IvanWilli/DemoKin" class="external-link">
+    <span class="fab fa-github fa-lg"></span>
+
+  </a>
+</li>
+      </ul></div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
 
 
-  </div>
-</nav><div class="container template-title-body">
-<div class="row">
-  <main id="main" class="col-md-9"><div class="page-header">
 
+      </header><div class="row">
+  <div class="contents col-md-9">
+    <div class="page-header">
       <h1>MIT License</h1>
-      <small class="dont-index">Source: <a href="https://github.com/IvanWilli/DemoKin/blob/HEAD/LICENSE.md" class="external-link"><code>LICENSE.md</code></a></small>
     </div>
 
 <div id="mit-license" class="section level1">
@@ -49,18 +82,26 @@ <h1>MIT License</h1>
 <p>THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
 </div>
 
-  </main></div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2>
+    </nav></div>
+
+</div>
+
 
 
-    <footer><div class="pkgdown-footer-left">
-  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+      <footer><div class="copyright">
+  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
 </div>
 
-    </footer></div>
+      </footer></div>
+
 
 
 
diff --git a/docs/authors.html b/docs/authors.html
index f77dc27..fd1a3c8 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,50 +1,83 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • DemoKin</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"></head><body>
-    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-
-
-    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
-
-    <a class="navbar-brand me-2" href="index.html">DemoKin</a>
-
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.0.3</small>
-
-
-    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
-      <span class="navbar-toggler-icon"></span>
-    </button>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Authors and Citation • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body data-spy="scroll" data-target="#toc">
+
+
+    <div class="container template-citation-authors">
+      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">DemoKin</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="">1.0.3</span>
+      </span>
+    </div>
 
-    <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="index.html"><span class="fa fas fa-home fa-lg"></span></a></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age"><li><a class="dropdown-item" href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a></li>
-    <li><a class="dropdown-item" href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a></li>
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav"><li>
+  <a href="index.html">
+    <span class="fas fa-home fa-lg"></span>
+
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Tutorials
+
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu"><li>
+      <a href="articles/1_1_OneSex_TimeInvariant_Age.html">One-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_2_OneSex_TimeVarying_Age.html">One-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/1_3_TwoSex_TimeInvariant_Age.html">Two-sex time-invariant model</a>
+    </li>
+    <li>
+      <a href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a>
+    </li>
+    <li>
+      <a href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant Age-Stage model</a>
+    </li>
+    <li>
+      <a href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying Age-Stage model</a>
+    </li>
   </ul></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-models-by-age-and-stage" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Models by Age and Stage</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-models-by-age-and-stage"><li><a class="dropdown-item" href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a></li>
-    <li><a class="dropdown-item" href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a></li>
-  </ul></li>
-      </ul><ul class="navbar-nav"><li class="nav-item"><a class="external-link nav-link" href="https://github.com/IvanWilli/DemoKin"><span class="fa fab fa-github fa-lg"></span></a></li>
-      </ul></div>
+<li>
+  <a href="reference/index.html">Functions</a>
+</li>
+      </ul><ul class="nav navbar-nav navbar-right"><li>
+  <a href="https://github.com/IvanWilli/DemoKin" class="external-link">
+    <span class="fab fa-github fa-lg"></span>
 
+  </a>
+</li>
+      </ul></div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
 
-  </div>
-</nav><div class="container template-citation-authors">
-<div class="row">
-  <main id="main" class="col-md-9"><div class="page-header">
 
-      <h1>Authors and Citation</h1>
-    </div>
 
-    <div class="section level2">
-      <h2>Authors</h2>
+      </header><div class="row">
+  <div class="contents col-md-9">
+    <div class="section level2 authors-section">
+      <div class="page-header">
+        <h1>Authors and Citation</h1>
+      </div>
+
 
       <ul class="list-unstyled"><li>
-          <p><strong>Iván Williams</strong>. Maintainer.
+          <p><strong>Iván Williams</strong>. Author, maintainer.
           </p>
         </li>
         <li>
@@ -59,39 +92,52 @@ <h2>Authors</h2>
           <p><strong>Caswell Hal</strong>. Contributor.
           </p>
         </li>
+        <li>
+          <p><strong>Benjamin Schlüter</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Butterick Joe</strong>. Contributor.
+          </p>
+        </li>
       </ul></div>
+    <div class="section level2 citation-section">
+    <div>
+      <h1 id="citation">Citation</h1>
+      <small class="dont-index">Source: <a href="https://github.com/IvanWilli/DemoKin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small>
+    </div>
+    </div>
 
-    <div class="section level2">
-      <h2 id="citation">Citation</h2>
-      <p><small class="dont-index">Source: <a href="https://github.com/IvanWilli/DemoKin/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-      <p>Alburez-Gutierrez D (2025).
+    <p>Williams I, Alburez-Gutierrez D (2025).
 <em>DemoKin: Estimate Population Kin Distribution</em>.
 https://github.com/IvanWilli/DemoKin,
 https://ivanwilli.github.io/DemoKin/.
 </p>
-      <pre>@Manual{,
+    <pre>@Manual{,
   title = {DemoKin: Estimate Population Kin Distribution},
-  author = {Diego Alburez-Gutierrez},
+  author = {Iván Williams and Diego Alburez-Gutierrez},
   year = {2025},
   note = {https://github.com/IvanWilli/DemoKin,
     https://ivanwilli.github.io/DemoKin/},
 }</pre>
-    </div>
 
-  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
-    </nav></aside></div>
+  </div>
+
+</div>
+
 
 
-    <footer><div class="pkgdown-footer-left">
-  <p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
+      <footer><div class="copyright">
+  <p></p><p>Developed by Iván Williams, Diego Alburez-Gutierrez.</p>
 </div>
 
-<div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
+<div class="pkgdown">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.9000.</p>
 </div>
 
-    </footer></div>
+      </footer></div>
+
 
 
 
diff --git a/docs/index.html b/docs/index.html
index ea8f86b..e70e20d 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -6,7 +6,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
 <title>Estimate Population Kin Distribution • DemoKin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/simplex/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
 <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
 <script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
@@ -22,7 +22,7 @@
 
 
     <div class="container template-home">
-      <header><div class="navbar navbar-reverse navbar-fixed-top" role="navigation">
+      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
   <div class="container">
     <div class="navbar-header">
       <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
@@ -65,15 +65,15 @@
       <a href="articles/1_4_TwoSex_TimeVarying_Age.html">Two-sex time-varying model</a>
     </li>
     <li>
-      <a href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant model</a>
+      <a href="articles/2_1_OneSex_TimeInvariant_AgeStage.html">One-sex time-invariant Age-Stage model</a>
     </li>
     <li>
-      <a href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying model</a>
+      <a href="articles/2_2_TwoSex_TimeVarying_AgeStage.html">Two-sex time-varying Age-Stage model</a>
     </li>
   </ul>
 </li>
 <li>
-  <a></a>
+  <a href="reference/index.html">Functions</a>
 </li>
       </ul>
 <ul class="nav navbar-nav navbar-right">
@@ -238,7 +238,7 @@ <h2 data-toc-skip>Citation</h2>
 <div class="developers">
 <h2 data-toc-skip>Developers</h2>
 <ul class="list-unstyled">
-<li>Iván Williams <br><small class="roles"> Maintainer </small>   </li>
+<li>Iván Williams <br><small class="roles"> Author, maintainer </small>   </li>
 <li>Diego Alburez-Gutierrez <br><small class="roles"> Author </small>   </li>
 <li><a href="authors.html">More about authors...</a></li>
 </ul>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 712db21..b958018 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -8,7 +8,7 @@ articles:
   1_4_TwoSex_TimeVarying_Age: 1_4_TwoSex_TimeVarying_Age.html
   2_1_OneSex_TimeInvariant_AgeStage: 2_1_OneSex_TimeInvariant_AgeStage.html
   2_2_TwoSex_TimeVarying_AgeStage: 2_2_TwoSex_TimeVarying_AgeStage.html
-last_built: 2025-04-25T17:34Z
+last_built: 2025-04-25T18:57Z
 urls:
   reference: https://ivanwilli.github.io/DemoKin/reference
   article: https://ivanwilli.github.io/DemoKin/articles

From ef487a48c6d900720b6b43506a37b400ad1791f4 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 25 Apr 2025 17:45:26 -0300
Subject: [PATCH 78/89] custom readme

---
 README.Rmd                                    | 20 +++----------
 README.md                                     | 28 ++++++-------------
 _pkgdown.yml                                  |  3 +-
 docs/404.html                                 |  2 +-
 docs/LICENSE-text.html                        |  2 +-
 docs/LICENSE.html                             |  2 +-
 docs/authors.html                             |  2 +-
 docs/index.html                               | 18 +++---------
 docs/pkgdown.yml                              |  2 +-
 vignettes/1_1_OneSex_TimeInvariant_Age.Rmd    |  8 ------
 vignettes/1_2_OneSex_TimeVarying_Age.Rmd      |  8 ------
 vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd    |  2 --
 vignettes/1_4_TwoSex_TimeVarying_Age.Rmd      |  2 --
 .../2_1_OneSex_TimeInvariant_AgeStage.Rmd     |  2 --
 vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd | 12 ++++----
 vignettes/references.bib                      | 12 ++++++++
 16 files changed, 40 insertions(+), 85 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 9c681a3..197fa26 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -11,12 +11,10 @@ knitr::opts_chunk$set(
   fig.path = "man/figures/README-",
   out.width = "100%"
 )
-devtools::load_all()
-library(DemoKin)
+pkgload::load_all()
 library(knitr)
 ```
 
-
 # DemoKin 
 
 ::: {.columns}
@@ -27,8 +25,8 @@ library(knitr)
 :::
 ::: {.column width="70%"}
 
-DemoKin is an R package for the demographic analysis of kinship networks using matrix-based models.  
-It implements methods developed by Caswell (2019) and colleagues (\ref{}) for estimating the number and age distribution of relatives under various demographic assumptions.
+This is an R package for the demographic analysis of kinship networks using matrix-based models.  
+It implements methods developed initially by [Caswell (2019)](https://www.demographic-research.org/articles/volume/41/24)), and continued in successive papers (like [Caswell (2020)](https://www.demographic-research.org/articles/volume/42/38) or [Caswell (2024)](https://www.demographic-research.org/articles/volume/49/41)) for estimating the number and age distribution of relatives under various demographic assumptions.
 
 :::
 :::
@@ -104,19 +102,9 @@ We thank Silvia Leek from the Max Planck Institute for Demographic Research for
 ## Get involved!
   
 `DemoKin` is under constant development.
-If you're interested in contributing, please get in touch, create an issue, or submit a pull request.
+If you're interested in contributing, please get in touch, create an [issue](https://github.com/IvanWilli/DemoKin/issues), or submit a pull request.
 We look forward to hearing from you!
 
-## References
-
-Caswell, H. (2019). The formal demography of kinship: A matrix formulation. Demographic Research, 41, 679-712.
-
-Caswell, H. (2020). The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research, 42, 1097-1144.
-
-Caswell, H. & Song, X. (2021). The formal demography of kinship III: Kinship dynamics with time-varying demographic rates. Demographic Research, 45, 517-546.
-
-Caswell, H. & Song, X. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359-396.
-
 ## License
 
 This project is licensed under the MIT License - see the LICENSE file for details.
diff --git a/README.md b/README.md
index 81d9e1b..000638c 100644
--- a/README.md
+++ b/README.md
@@ -13,9 +13,14 @@
 
 <div class="column" width="70%">
 
-DemoKin is an R package for the demographic analysis of kinship networks
+This is an R package for the demographic analysis of kinship networks
 using matrix-based models.  
-It implements methods developed by Caswell (2019) and colleagues () for
+It implements methods developed initially by [Caswell
+(2019)](https://www.demographic-research.org/articles/volume/41/24)),
+and continued in successive papers (like [Caswell
+(2020)](https://www.demographic-research.org/articles/volume/42/38) or
+[Caswell
+(2024)](https://www.demographic-research.org/articles/volume/49/41)) for
 estimating the number and age distribution of relatives under various
 demographic assumptions.
 
@@ -114,25 +119,10 @@ Commons. Sha Jiang provided useful comments for improving the package.
 ## Get involved!
 
 `DemoKin` is under constant development. If you’re interested in
-contributing, please get in touch, create an issue, or submit a pull
+contributing, please get in touch, create an
+[issue](https://github.com/IvanWilli/DemoKin/issues), or submit a pull
 request. We look forward to hearing from you!
 
-## References
-
-Caswell, H. (2019). The formal demography of kinship: A matrix
-formulation. Demographic Research, 41, 679-712.
-
-Caswell, H. (2020). The formal demography of kinship II: Multistate
-models, parity, and sibship. Demographic Research, 42, 1097-1144.
-
-Caswell, H. & Song, X. (2021). The formal demography of kinship III:
-Kinship dynamics with time-varying demographic rates. Demographic
-Research, 45, 517-546.
-
-Caswell, H. & Song, X. (2022). The formal demography of kinship IV:
-Two-sex models and their approximations. Demographic Research, 47,
-359-396.
-
 ## License
 
 This project is licensed under the MIT License - see the LICENSE file
diff --git a/_pkgdown.yml b/_pkgdown.yml
index c5d9973..a3fdefd 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -2,8 +2,7 @@ url: https://ivanwilli.github.io/DemoKin/
 template:
   params:
     bootstrap: 5
-    bootswatch: minty
-    theme: breeze-light
+    bootswatch: simplex
 
 navbar:
   title: "DemoKin"
diff --git a/docs/404.html b/docs/404.html
index d9a2111..7f90524 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -6,7 +6,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
 <title>Page not found (404) • DemoKin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/simplex/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
 <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="https://ivanwilli.github.io/DemoKin/bootstrap-toc.css">
 <script src="https://ivanwilli.github.io/DemoKin/bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index 19092ed..fb6f9f5 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>License • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="License"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>License • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/simplex/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="License"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body data-spy="scroll" data-target="#toc">
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index 75d8d27..be8a489 100644
--- a/docs/LICENSE.html
+++ b/docs/LICENSE.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>MIT License • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/simplex/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="MIT License"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--></head><body data-spy="scroll" data-target="#toc">
diff --git a/docs/authors.html b/docs/authors.html
index fd1a3c8..5474681 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Authors and Citation • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Authors and Citation • DemoKin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/simplex/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css"><script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet"><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--></head><body data-spy="scroll" data-target="#toc">
diff --git a/docs/index.html b/docs/index.html
index e70e20d..41587b5 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -6,7 +6,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
 <title>Estimate Population Kin Distribution • DemoKin</title>
-<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/litera/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.1/jquery.min.js" integrity="sha512-v2CJ7UaYy4JwqLDIrZUI/4hqeoQieOmAZNXBeQyjo21dadnwR+8ZaIJVT8EE2iyI61OV8e6M8PP2/4hpQINQ/g==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/simplex/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
 <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
 <script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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@@ -104,7 +104,7 @@
 </div>
 <div class="column" style="width:70%;">
 <p>DemoKin is an R package for the demographic analysis of kinship networks using matrix-based models.<br>
-It implements methods developed by Caswell and colleagues for estimating the number and age distribution of relatives under various demographic assumptions.</p>
+It implements methods developed by initially by <a href="https://www.demographic-research.org/articles/volume/41/24" class="external-link">Caswell (2019)</a>), and continued in successive papers (like <a href="https://www.demographic-research.org/articles/volume/42/38" class="external-link">Caswell (2020)</a>) for estimating the number and age distribution of relatives under various demographic assumptions.</p>
 </div>
 </div>
 <div class="section level2">
@@ -150,9 +150,7 @@ <h2 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a>
 <span>    title <span class="op">=</span> <span class="st">"Expected number of living relatives by age"</span>,</span>
 <span>    x <span class="op">=</span> <span class="st">"Age of focal individual"</span>,</span>
 <span>    y <span class="op">=</span> <span class="st">"Number of relatives"</span></span>
-<span>  <span class="op">)</span></span>
-<span><span class="co">#&gt; Joining with `by = join_by(kin)`</span></span></code></pre></div>
-<p><img src="reference/figures/README-unnamed-chunk-3-1.png" width="100%"></p>
+<span>  <span class="op">)</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a>
@@ -191,15 +189,7 @@ <h2 id="acknowledgments">Acknowledgments<a class="anchor" aria-label="anchor" hr
 <div class="section level2">
 <h2 id="get-involved">Get involved!<a class="anchor" aria-label="anchor" href="#get-involved"></a>
 </h2>
-<p><code>DemoKin</code> is under constant development. If you’re interested in contributing, please get in touch, create an issue, or submit a pull request. We look forward to hearing from you!</p>
-</div>
-<div class="section level2">
-<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
-</h2>
-<p>Caswell, H. (2019). The formal demography of kinship: A matrix formulation. Demographic Research, 41, 679-712.</p>
-<p>Caswell, H. (2020). The formal demography of kinship II: Multistate models, parity, and sibship. Demographic Research, 42, 1097-1144.</p>
-<p>Caswell, H. &amp; Song, X. (2021). The formal demography of kinship III: Kinship dynamics with time-varying demographic rates. Demographic Research, 45, 517-546.</p>
-<p>Caswell, H. &amp; Song, X. (2022). The formal demography of kinship IV: Two-sex models and their approximations. Demographic Research, 47, 359-396.</p>
+<p><code>DemoKin</code> is under constant development. If you’re interested in contributing, please get in touch, create an <a href="https://github.com/IvanWilli/DemoKin/issues" class="external-link">issue</a>, or submit a pull request. We look forward to hearing from you!</p>
 </div>
 <div class="section level2">
 <h2 id="license">License<a class="anchor" aria-label="anchor" href="#license"></a>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index b958018..5af90af 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -8,7 +8,7 @@ articles:
   1_4_TwoSex_TimeVarying_Age: 1_4_TwoSex_TimeVarying_Age.html
   2_1_OneSex_TimeInvariant_AgeStage: 2_1_OneSex_TimeInvariant_AgeStage.html
   2_2_TwoSex_TimeVarying_AgeStage: 2_2_TwoSex_TimeVarying_AgeStage.html
-last_built: 2025-04-25T18:57Z
+last_built: 2025-04-25T20:32Z
 urls:
   reference: https://ivanwilli.github.io/DemoKin/reference
   article: https://ivanwilli.github.io/DemoKin/articles
diff --git a/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd b/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
index 9f45037..1f7aa31 100644
--- a/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
+++ b/vignettes/1_1_OneSex_TimeInvariant_Age.Rmd
@@ -97,8 +97,6 @@ install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
 install.packages("knitr")     # Document generation
-install.packages("data.table")# Efficient data handling
-install.packages("Matrix")    # Matrix operations
 
 # Install DemoKin
 # DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
@@ -119,12 +117,6 @@ library(ggplot2)  # For visualization
 library(knitr)    # For document generation
 ```
 
-<!-- Load additional utility functions that we've prepared for this tutorial: -->
-
-<!-- ```{r functions, warning=F, message=FALSE} -->
-<!-- source("functions.R") -->
-<!-- ``` -->
-
 # Understanding the Demographic Data {#demographic-data}
 
 ## Data Overview
diff --git a/vignettes/1_2_OneSex_TimeVarying_Age.Rmd b/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
index 1f3d488..2342a12 100644
--- a/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
+++ b/vignettes/1_2_OneSex_TimeVarying_Age.Rmd
@@ -96,8 +96,6 @@ install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
 install.packages("knitr")     # Document generation
-install.packages("data.table")# Efficient data handling
-install.packages("Matrix")    # Matrix operations
 
 # Install DemoKin
 # DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
@@ -118,12 +116,6 @@ library(ggplot2)  # For visualization
 library(knitr)    # For document generation
 ```
 
-<!-- Load the utility functions we've prepared: -->
-
-<!-- ```{r functions, warning=F, message=FALSE} -->
-<!-- source("functions.R") -->
-<!-- ``` -->
-
 # Time-Varying Kinship Models {#time-varying-models}
 
 ## The Conceptual Shift
diff --git a/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd b/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
index fc5f08c..6f949c1 100644
--- a/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
+++ b/vignettes/1_3_TwoSex_TimeInvariant_Age.Rmd
@@ -96,8 +96,6 @@ install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
 install.packages("knitr")     # Document generation
-install.packages("data.table")# Efficient data handling
-install.packages("Matrix")    # Matrix operations
 
 # Install DemoKin
 # DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
diff --git a/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd b/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
index e593f40..da47edb 100644
--- a/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
+++ b/vignettes/1_4_TwoSex_TimeVarying_Age.Rmd
@@ -86,8 +86,6 @@ install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
 install.packages("knitr")     # Document generation
-install.packages("data.table")# Efficient data handling
-install.packages("Matrix")    # Matrix operations
 
 # Install DemoKin
 # DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
diff --git a/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd b/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
index e96b306..a8c6ee9 100644
--- a/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
+++ b/vignettes/2_1_OneSex_TimeInvariant_AgeStage.Rmd
@@ -96,8 +96,6 @@ install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
 install.packages("knitr")     # Document generation
-install.packages("data.table")# Efficient data handling
-install.packages("Matrix")    # Matrix operations
 
 # Install DemoKin
 # DemoKin is available on CRAN (https://cran.r-project.org/web/packages/DemoKin/index.html), 
diff --git a/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd b/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
index 07eb591..552f9ab 100644
--- a/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
+++ b/vignettes/2_2_TwoSex_TimeVarying_AgeStage.Rmd
@@ -81,7 +81,9 @@ In this final vignette, we integrate all elements from previous tutorials into t
 - Stage-based transitions (like parity or education)
 - Joint distributions of age, sex, and stage among relatives
 
-Building on Caswell and colleagues' theoretical developments [-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2021; -@caswell_formal_2022], this approach provides unprecedented analytical power for understanding complex family structures. We'll implement this comprehensive model using the `kin_multi_stage_time_variant_2sex` function, exploring two examples: one using parity and another using educational attainment to demonstrate how these models illuminate contemporary family dynamics in ways simpler models cannot.
+
+
+Building on previous Caswell and colleagues' theoretical developments [-@caswell_formal_2019; -@caswell_formal_2020; -@caswell_formal_2021; -@caswell_formal_2022], Butterick and others [-@Butterick2025] created an integral approach providing unprecedented analytical power for understanding complex family structures. We'll apply this comprehensive model using the `kin_multi_stage_time_variant_2sex` function, exploring two examples: one using parity and another using educational attainment to demonstrate how these models illuminate contemporary family dynamics in ways simpler models cannot.
 
 ## Package Installation {#preparation}
 
@@ -93,7 +95,6 @@ install.packages("dplyr")     # Data manipulation
 install.packages("tidyr")     # Data tidying
 install.packages("ggplot2")   # Data visualization
 install.packages("knitr")     # Document generation
-install.packages("data.table")# Efficient data handling
 install.packages("Matrix")    # Matrix operations
 
 # Install DemoKin
@@ -115,9 +116,7 @@ library(tidyr)    # For restructuring data
 library(ggplot2)  # For visualization
 library(knitr)    # For document generation
 library(Matrix)   # For matrix operations
-library(tictoc)   # For timing operations
 options(dplyr.summarise.inform = FALSE) # hide summarise output
-# source("kin_multi_stage_time_variant_2sex.R")
 ```
 
 # Two-Sex Time-Varying Multi-State Models {#two-sex-time-varying-multi-state}
@@ -182,6 +181,8 @@ These lists contain period-specific demographic rates:
 
 Now let's implement the two-sex time-varying multi-state model with parity as the stage variable:
 
+> Note: This model run takes approximately 30 minutes to complete, so we let to the reader its run. 
+
 ```{r parity_model, message=FALSE, warning=FALSE, eval=FALSE}
 # Define time period and parameters
 no_years <- 40  # Run the model for 40 years (1965-2005)
@@ -207,8 +208,6 @@ kin_out_1965_2005 <-
   )
 ```
 
-> Note: This model run takes approximately 30 minutes to complete. 
-
 Now we need to recode the stage variables to show meaningful parity labels:
 
 ```{r parity_output_recode, message=FALSE, warning=FALSE, eval=FALSE}
@@ -450,7 +449,6 @@ kin_out_2020_2090 <-
     summary_kin = TRUE,                                  # Produce summary statistics
     sex_Focal = "Female",                                # Focal is female
     initial_stage_Focal = 1,                             # Focal starts with no education
-    # model_years = output_year,                            # the sequence of years we model
     output_years = output_year                           # All years
   )
 ```
diff --git a/vignettes/references.bib b/vignettes/references.bib
index 43cee86..25c5b53 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -1,3 +1,15 @@
+@article{Butterick2025,
+title = {A mathematical framework for time-variant multi-state kinship modelling},
+journal = {Theoretical Population Biology},
+volume = {163},
+pages = {1-12},
+year = {2025},
+issn = {0040-5809},
+doi = {https://doi.org/10.1016/j.tpb.2025.02.002},
+url = {https://www.sciencedirect.com/science/article/pii/S0040580925000103},
+author = {Joe W.B. Butterick and Peter W.F. Smith and Jakub Bijak and Jason Hilton}
+}
+
 @book{Keyfitz2005,
   title={Applied mathematical demography},
   author={Keyfitz, Nathan and Caswell, Hal and others},

From 8e5f69439d52d330753bd4a5f1600952d95daccc Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Tue, 29 Apr 2025 21:01:54 -0300
Subject: [PATCH 79/89] add ctbs

---
 DESCRIPTION  |  3 ++-
 README.Rmd   | 20 ++++++++++++++++++++
 README.md    | 33 +++++++++++++++++++++++++++++++++
 _pkgdown.yml |  2 +-
 4 files changed, 56 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 561a3d1..bc7d22f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -10,7 +10,8 @@ Authors@R: c(
     person("Xi", "Song", email = "xisong@sas.upenn.edu", role = "ctb"),
     person("Caswell", "Hal", email = "caswell@demogr.mpg.de", role = "ctb"),
     person("Benjamin", "Schlüter", email = "benjamin.schluter@utoronto.ca", role = "ctb"),
-    person("Butterick", "Joe", email = "J.Butterick@soton.ac.uk", role = "ctb"))
+    person("Butterick", "Joe", email = "J.Butterick@soton.ac.uk", role = "ctb"),
+    person("Jiang", "Sha", email = "jiang@demogr.mpg.de", role = "ctb"))
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
diff --git a/README.Rmd b/README.Rmd
index 197fa26..f95c541 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -91,6 +91,26 @@ The site includes several vignettes demonstrating different types of kinship mod
 - [One-sex time-invariant multi-state model](https://ivanwilli.github.io/DemoKin/articles/2_1_OneSex_TimeInvariant_AgeStage.html)
 - [Two-sex time-varying multi-state model](https://ivanwilli.github.io/DemoKin/articles/2_2_TwoSex_TimeVarying_AgeStage.html)
 
+## Contributors
+
+`DemoKin` has benefited from the work of many people over the years, including:
+
+- **Ivan Williams** (University of Buenos Aires) leads the technical development of `DemoKin` with a particular focus on the implementation of the functions in R.
+
+- **Diego Alburez-Gutierrez** (Max Planck Institute for Demographic Research) coordinates the development of the package.
+
+- **Hal Caswell** (University of Amsterdam) provided crucial MATLAB code for computing matrix kinship models.
+
+- **Shi Song** (University of Pennsylvania) contributed the code to estimate time variant kinship models.
+
+- **Benjamin Schlüter** (University of Toronto) contributed code to implement bereavement analysis by cause of death.
+
+- **Joe Butterick** (University of Southampton) contributed code to implement time-variant, two-sex  multistate kinship models.
+
+- **Sha Jiang** (Max Planck Institute for Demographic Research) helped create and organise the documention for the package, including developing the package’s website.
+
+- **Amanda Martins** (Max Planck Institute for Demographic Research) developed materials for teaching DemoKin, including a course at the [European Doctoral School of Demography]( https://amandamartinsal.github.io/EDSD_kinship_24-25/).
+
 ## Citation
 
 Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team. (2021) DemoKin: An R package to implement demographic matrix kinship models. URL: https://github.com/IvanWilli/DemoKin.
diff --git a/README.md b/README.md
index 000638c..e71c9ea 100644
--- a/README.md
+++ b/README.md
@@ -100,6 +100,39 @@ kinship models:
 - [Two-sex time-varying multi-state
   model](https://ivanwilli.github.io/DemoKin/articles/2_2_TwoSex_TimeVarying_AgeStage.html)
 
+## Contributors
+
+`DemoKin` has benefited from the work of many people over the years,
+including:
+
+- **Ivan Williams** (University of Buenos Aires) leads the technical
+  development of `DemoKin` with a particular focus on the implementation
+  of the functions in R.
+
+- **Diego Alburez-Gutierrez** (Max Planck Institute for Demographic
+  Research) coordinates the development of the package.
+
+- **Hal Caswell** (University of Amsterdam) provided crucial MATLAB code
+  for computing matrix kinship models.
+
+- **Shi Song** (University of Pennsylvania) contributed the code to
+  estimate time variant kinship models.
+
+- **Benjamin Schlüter** (University of Toronto) contributed code to
+  implement bereavement analysis by cause of death.
+
+- **Joe Butterick** (University of Southampton) contributed code to
+  implement time-variant, two-sex multistate kinship models.
+
+- **Sha Jiang** (Max Planck Institute for Demographic Research) helped
+  create and organise the documention for the package, including
+  developing the package’s website.
+
+- **Amanda Martins** (Max Planck Institute for Demographic Research)
+  developed materials for teaching DemoKin, including a course at the
+  [European Doctoral School of
+  Demography](https://amandamartinsal.github.io/EDSD_kinship_24-25/).
+
 ## Citation
 
 Williams, Iván; Alburez-Gutierrez, Diego; and the DemoKin team. (2021)
diff --git a/_pkgdown.yml b/_pkgdown.yml
index a3fdefd..5486625 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -2,7 +2,7 @@ url: https://ivanwilli.github.io/DemoKin/
 template:
   params:
     bootstrap: 5
-    bootswatch: simplex
+    bootswatch: united
 
 navbar:
   title: "DemoKin"

From b861994a33dc9166d9cbadd494429dfa54dbad7f Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 14 May 2025 14:46:35 -0300
Subject: [PATCH 80/89] corr authors/colabs

---
 DESCRIPTION | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index bc7d22f..eb23328 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -8,10 +8,10 @@ Authors@R: c(
     person("Iván", "Williams", email = "act.ivanwilliams@gmail.com", role = c("aut", "cre")),
     person("Diego", "Alburez-Gutierrez", email = "alburezgutierrez@demogr.mpg.de", role = "aut"),
     person("Xi", "Song", email = "xisong@sas.upenn.edu", role = "ctb"),
-    person("Caswell", "Hal", email = "caswell@demogr.mpg.de", role = "ctb"),
+    person("Hal", "Caswell", email = "caswell@demogr.mpg.de", role = "ctb"),
     person("Benjamin", "Schlüter", email = "benjamin.schluter@utoronto.ca", role = "ctb"),
-    person("Butterick", "Joe", email = "J.Butterick@soton.ac.uk", role = "ctb"),
-    person("Jiang", "Sha", email = "jiang@demogr.mpg.de", role = "ctb"))
+    person("Joe", "Butterick", email = "J.Butterick@soton.ac.uk", role = "ctb"),
+    person("Sha", "Jiang", email = "jiang@demogr.mpg.de", role = "ctb"))
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true

From a5a428a87fc0a8c54215cf6d4bbb2071a646d65c Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Wed, 14 May 2025 14:55:01 -0300
Subject: [PATCH 81/89] auth corr

---
 README.Rmd | 2 +-
 README.md  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index f95c541..15cd9da 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -101,7 +101,7 @@ The site includes several vignettes demonstrating different types of kinship mod
 
 - **Hal Caswell** (University of Amsterdam) provided crucial MATLAB code for computing matrix kinship models.
 
-- **Shi Song** (University of Pennsylvania) contributed the code to estimate time variant kinship models.
+- **Xi Song** (University of Pennsylvania) contributed the code to estimate time variant kinship models.
 
 - **Benjamin Schlüter** (University of Toronto) contributed code to implement bereavement analysis by cause of death.
 
diff --git a/README.md b/README.md
index e71c9ea..e1b4958 100644
--- a/README.md
+++ b/README.md
@@ -115,7 +115,7 @@ including:
 - **Hal Caswell** (University of Amsterdam) provided crucial MATLAB code
   for computing matrix kinship models.
 
-- **Shi Song** (University of Pennsylvania) contributed the code to
+- **Xi Song** (University of Pennsylvania) contributed the code to
   estimate time variant kinship models.
 
 - **Benjamin Schlüter** (University of Toronto) contributed code to

From 600c877b5e3a2152822846dd89282b1e55c51be0 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Fri, 16 May 2025 18:06:40 -0300
Subject: [PATCH 82/89] add coll

---
 README.Rmd | 4 +++-
 README.md  | 7 +++++--
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 15cd9da..50c6f06 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -26,7 +26,7 @@ library(knitr)
 ::: {.column width="70%"}
 
 This is an R package for the demographic analysis of kinship networks using matrix-based models.  
-It implements methods developed initially by [Caswell (2019)](https://www.demographic-research.org/articles/volume/41/24)), and continued in successive papers (like [Caswell (2020)](https://www.demographic-research.org/articles/volume/42/38) or [Caswell (2024)](https://www.demographic-research.org/articles/volume/49/41)) for estimating the number and age distribution of relatives under various demographic assumptions.
+It implements methods developed initially by [Caswell (2019)](https://www.demographic-research.org/articles/volume/41/24), and continued in successive papers (like [Caswell (2020)](https://www.demographic-research.org/articles/volume/42/38) or [Caswell (2024)](https://www.demographic-research.org/articles/volume/49/41)) for estimating the number and age distribution of relatives under various demographic assumptions.
 
 :::
 :::
@@ -107,6 +107,8 @@ The site includes several vignettes demonstrating different types of kinship mod
 
 - **Joe Butterick** (University of Southampton) contributed code to implement time-variant, two-sex  multistate kinship models.
 
+- **Jason Hilton** (University of Southampton) contributed code to implement time-variant, two-sex multistate kinship models.
+
 - **Sha Jiang** (Max Planck Institute for Demographic Research) helped create and organise the documention for the package, including developing the package’s website.
 
 - **Amanda Martins** (Max Planck Institute for Demographic Research) developed materials for teaching DemoKin, including a course at the [European Doctoral School of Demography]( https://amandamartinsal.github.io/EDSD_kinship_24-25/).
diff --git a/README.md b/README.md
index e1b4958..51c4aab 100644
--- a/README.md
+++ b/README.md
@@ -16,8 +16,8 @@
 This is an R package for the demographic analysis of kinship networks
 using matrix-based models.  
 It implements methods developed initially by [Caswell
-(2019)](https://www.demographic-research.org/articles/volume/41/24)),
-and continued in successive papers (like [Caswell
+(2019)](https://www.demographic-research.org/articles/volume/41/24), and
+continued in successive papers (like [Caswell
 (2020)](https://www.demographic-research.org/articles/volume/42/38) or
 [Caswell
 (2024)](https://www.demographic-research.org/articles/volume/49/41)) for
@@ -124,6 +124,9 @@ including:
 - **Joe Butterick** (University of Southampton) contributed code to
   implement time-variant, two-sex multistate kinship models.
 
+- **Jason Hilton** (University of Southampton) contributed code to
+  implement time-variant, two-sex multistate kinship models.
+
 - **Sha Jiang** (Max Planck Institute for Demographic Research) helped
   create and organise the documention for the package, including
   developing the package’s website.

From d168692ede731689212ca974bd8e65ef28b47c78 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 22 May 2025 09:03:10 -0300
Subject: [PATCH 83/89] papers

---
 _pkgdown.yml             |  5 +++-
 vignettes/0_0_Papers.Rmd | 64 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 68 insertions(+), 1 deletion(-)
 create mode 100644 vignettes/0_0_Papers.Rmd

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 5486625..16daf3e 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -7,7 +7,7 @@ template:
 navbar:
   title: "DemoKin"
   structure:
-    left: [home, tutorials, reference]
+    left: [home, tutorials, reference, articles]
     right: [github]
   components:
     home:
@@ -34,6 +34,9 @@ navbar:
     reference:
       text: "Functions"
       href: reference/index.html
+    articles:
+      text: "Functions"
+      href: articles/0_0_Papers.html
     github:
       icon: fab fa-github fa-lg
       href: https://github.com/IvanWilli/DemoKin
diff --git a/vignettes/0_0_Papers.Rmd b/vignettes/0_0_Papers.Rmd
new file mode 100644
index 0000000..ace0760
--- /dev/null
+++ b/vignettes/0_0_Papers.Rmd
@@ -0,0 +1,64 @@
+---
+title: "Papers using DemoKin"
+output:
+  html_document:
+    toc: true
+    toc_float: 
+      collapsed: false
+      smooth_scroll: true
+    theme: readable
+    highlight: pygments
+    df_print: paged
+    fig_caption: true
+---
+
+## Published Articles and Pre-prints using DemoKin
+
+Acosta, E., Alburez-Gutierrez, D., Gargiulo, M., & Torres, C. (2025). *Weaponizing kinship: A demographic analysis of bereavement in the Colombian conflict* (Version 1) [Preprint]. SocArXiv. <https://osf.io/p87aw>
+
+Alburez-Gutierrez, D., Acosta, E., Zagheni, E., & Williams, N. E. (2024). The long-lasting effect of armed-conflict deaths on the living: Quantifying family bereavement. *Science Advances, 10*(30), eado6951. <https://doi.org/10.1126/sciadv.ado6951>
+
+Alburez-Gutierrez, D., Basellini, U., & Zagheni, E. (2024). When do mothers bury a child? Heterogeneity in the maternal age at offspring loss across the demographic transition. *Population Studies*. Advance online publication. <https://doi.org/10.1080/00324728.2024.2345075>
+
+Alburez-Gutierrez, D., Williams, I., & Caswell, H. (2023). Projections of human kinship for all countries. *Proceedings of the National Academy of Sciences, 120*(52), e2315722120. <https://doi.org/10.1073/pnas.2315722120>
+
+Adhikari, S., & Alburez-Gutierrez, D. (2025). *The future of grandparenthood in South Asia: The role of population aging and educational expansion* (MPIDR Working Paper WP-2025-003). Max Planck Institute for Demographic Research. <https://doi.org/10.4054/MPIDR-WP-2025-003>
+
+Ellis, S., Franks, D. W., Nielsen, M. L. K., Weiss, M. N., Croft, D. P., *et al.* (2024). The evolution of menopause in toothed whales. *Nature, 627*, 579–585. <https://doi.org/10.1038/s41586-024-07159-9>
+
+Feng, K., Song, X., & Caswell, H. (2024). Kinship and care: Racial disparities in potential dementia caregiving in the United States from 2000 to 2060. *The Journals of Gerontology: Series A, 79*(Suppl 1), S32–S41. <https://doi.org/10.1093/gerona/glae106>
+
+Hünteler, B. M., Polizzi, A., & van Raalte, A. A. (2024). *Changing kinship structures in East and West Germany before and after reunification* [Preprint]. SocArXiv. <https://doi.org/10.31235/osf.io/6bf7k>
+
+Jiang, S., Zuo, W., Guo, Z., Caswell, H., & Tuljapurkar, S. (2023). How does the demographic transition affect kinship networks? *Demographic Research, 48*, 899–930. <https://doi.org/10.4054/DemRes.2023.48.32>
+
+Jiang, S., Zuo, W., Guo, Z., & Tuljapurkar, S. (in press). Changing demographic rates reshape kinship networks. *Demography*.
+
+Matos-Moreno, A., Alburez-Gutierrez, D., Verdery, A. M., Santos-Lozada, A. R., Fernández Soto, M., & Williams, I. (2025). Kinship structures for left-behind older adults in high-out-migration contexts: Evidence from Puerto Rico. *The Journals of Gerontology: Series B, 80*(6), gbaf052. <https://doi.org/10.1093/geronb/gbaf052>
+
+Robles, A., Martes-Camargo, P., & Rodríguez-Franco, R. (2024). ¿Quién cuida a la niña? Provisión de cuidados y estructura de parentesco en América Latina [Who takes care of the girl? Care provision and kinship structure in Latin America]. *Revista Brasileira de Estudos de População, 41*, e0274, 1–24. <https://doi.org/10.20947/S0102-3098a0274>
+
+Schlüter, B.-S., Alburez-Gutierrez, D., Bibbins-Domingo, K., Alexander, M. J., & Kiang, M. V. (2024). Youth experiencing parental death due to drug poisoning and firearm violence in the US, 1999–2020. *JAMA, 331*(20), 1741–1747. <https://doi.org/10.1001/jama.2024.8391>
+
+Snyder, M., Alburez-Gutierrez, D., Williams, I., & Zagheni, E. (2022). Estimates from 31 countries show the significant impact of COVID-19 excess mortality on the incidence of family bereavement. *Proceedings of the National Academy of Sciences of the United States of America, 119*(26), e2202686119. <https://doi.org/10.1073/pnas.2202686119>
+
+Song, X., & Caswell, H. (2022). The role of kinship in racial differences in exposure to unemployment. *Demography, 59*(4), 1325–1352. <https://doi.org/10.1215/00703370-10057831>
+
+van Damme, M., Alburez-Gutierrez, D., & Castro, A. (2025). Estimating kinship size of older adults in Europe with models and surveys. *Demography*, 1–1961236. <https://doi.org/10.1215/00703370-11961236>
+
+## Formal demography papers
+
+Butterick, J. W. B., Smith, P. W. F., Bijak, J., & Hilton, J. (2025). A mathematical framework for time-variant multi-state kinship modelling. *Theoretical Population Biology, 163*, 1–12. <https://doi.org/10.1016/j.tpb.2025.02.002>
+
+
+Caswell, H. (2019). The formal demography of kinship: A matrix formulation. *Demographic Research, 41*(24), 679–712. <https://doi.org/10.4054/DemRes.2019.41.24>
+
+Caswell, H. (2020). The formal demography of kinship II: Multistate models, parity, and sibship. *Demographic Research, 42*(38), 1097–1146. <https://doi.org/10.4054/DemRes.2020.42.38>
+
+Caswell, H., & Song, X. (2021). The formal demography of kinship III: Kinship dynamics with time-varying demographic rates. *Demographic Research, 45*(16), 517–546. <https://doi.org/10.4054/DemRes.2021.45.16>
+
+Caswell, H. (2022). The formal demography of kinship IV: Two-sex models and their approximations. *Demographic Research, 47*(13), 359–396. <https://doi.org/10.4054/DemRes.2022.47.13>
+
+Caswell, H., Margolis, R., & Verdery, A. M. (2023). The formal demography of kinship V: Kin loss, bereavement, and causes of death. *Demographic Research, 49*(41), 1163–1200. <https://doi.org/10.4054/DemRes.2023.49.41>
+
+Caswell, H. (2024). The formal demography of kinship VI: Demographic stochasticity and variance in the kinship network. *Demographic Research, 51*(39), 1201–1256. <https://doi.org/10.4054/DemRes.2024.51.39>

From f2eb38770fe5138d524fc11cfe8741036d0b88b4 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 22 May 2025 09:17:58 -0300
Subject: [PATCH 84/89] papers

---
 _pkgdown.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_pkgdown.yml b/_pkgdown.yml
index 16daf3e..164bfbc 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -35,7 +35,7 @@ navbar:
       text: "Functions"
       href: reference/index.html
     articles:
-      text: "Functions"
+      text: "Articles"
       href: articles/0_0_Papers.html
     github:
       icon: fab fa-github fa-lg

From 2e382fb79afbf0b9b8080c789f0f8d8299899971 Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Tue, 3 Jun 2025 11:33:52 +0100
Subject: [PATCH 85/89] Add files via upload

---
 kin_multi_stage_time_variant_2sex.R | 1061 +++++++++++++++++++++++++++
 1 file changed, 1061 insertions(+)
 create mode 100644 kin_multi_stage_time_variant_2sex.R

diff --git a/kin_multi_stage_time_variant_2sex.R b/kin_multi_stage_time_variant_2sex.R
new file mode 100644
index 0000000..6b51be4
--- /dev/null
+++ b/kin_multi_stage_time_variant_2sex.R
@@ -0,0 +1,1061 @@
+
+#' Estimate kin counts by age, stage, and sex, in a time variant framework
+
+#' @description Implementation of combined formal demographic models: Caswell II,III,IV.
+
+#' @param U_list_females list with matrix entries: period-specific female survival probabilities. Age in rows and states in columns.
+#' @param U_list_males list with matrix entries: period-specific male survival probabilities. Age in rows and states in columns.
+#' @param F_list_females list with matrix with elements: period-specific female fertility (age in rows and states in columns).
+#' @param F_list_males list with matrix entries: period-specific male fertility (age in rows and states in columns).
+#' @param T_list_females list of lists with matrix entries: each outer list entry is period-specific, and composed of
+#'                     a list of stochastic matrices which describe age-specific female probabilities of transferring stage
+#' @param T_list_males list of lists with matrix entries: each outer list entry is period-specific, and composed of
+#'                     a list of stochastic matrices which describe age-specific male probabilities of transferring stage
+#' @param H_list list with matrix entries: redistribution of newborns across each stage to a specific age-class
+#' @param birth_female numeric. birth ratio of females to males in population
+#' @param parity logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default `TRUE`.
+#' @param output_kin vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.
+#' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if TRUE, and kin by their age*stage distribution by age of Focal if FALSE.
+#' @param sex_Focal character. Female or Male as the user requests.
+#' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
+#' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
+#'
+#' @return A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
+
+#' @export
+#'
+kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
+                                              U_list_males = NULL,
+                                              F_list_females = NULL,
+                                              F_list_males = NULL,
+                                              T_list_females = NULL,
+                                              T_list_males = NULL,
+                                              H_list = NULL,
+                                              birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
+                                              parity = FALSE,
+                                              output_kin = NULL, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
+                                              summary_kin = TRUE, # Set to FALSE if we want only a full age*stage distribution of kin
+                                              sex_Focal = "Female", # Female Focal is default
+                                              initial_stage_Focal = NULL,
+                                              output_years){
+
+  no_years <- length(U_list_females)
+  na <- nrow(U_list_females[[1]])
+  ns <- ncol(U_list_females[[1]])
+
+  # Ensure inputs are lists of matrices and that the timescale same length
+  # if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
+  if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+  if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+  if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
+
+  ### Define empty lists for the accumulated kin of Focals's life-course -- each list entry will reflect a time-period
+  changing_pop_struct <- list()
+  Focal_array <- list()
+  mom_array <- list()
+  gran_array <- list()
+  great_gran_array <- list()
+  daughter_array <- list()
+  younger_sis_array <- list()
+  grand_daughter_array <-list()
+  great_grand_daughter_array <- list()
+  older_sister_array <- list()
+  younger_aunt_array <- list()
+  older_aunt_array <- list()
+  younger_niece_array <- list()
+  older_niece_array <- list()
+  younger_cousin_array <- list()
+  older_cousin_array <- list()
+
+  ### At each time-period we: 1) -- construct the time-variant projection matrices:
+  ###                               U_tilde : transfers across stage and advances age
+  ###                               F_tilde : makes newborns from stage/age; puts them to stage/age
+  ###                         2) -- project Focal and kin using above projection matrices
+
+  pb <- progress::progress_bar$new(
+    format = "Timescale [:bar] :percent",
+    total = no_years + 1, clear = FALSE, width = 60)
+  tictoc::tic()
+  for(year in 1:no_years){
+    T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
+    T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
+    T_f_list <- list()
+    T_m_list <- list()
+    F_f_list <- list()
+    F_m_list <- list()
+    U_f_list <- list()
+    U_m_list <- list()
+    H_list2 <- list()
+
+    for(stage in 1:ns){
+      Uf <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      Matrix::diag(Uf[-1,-ncol(Uf)]) <- U_list_females[[year]][1:(na-1),stage]
+      Uf[na,na] <- U_list_females[[year]][na,stage]
+      Um <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      Matrix::diag(Um[-1,-ncol(Um)]) <- U_list_males[[year]][1:(na-1),stage]
+      Um[na,na] <- U_list_males[[year]][na,stage]
+      U_f_list[[(1+length(U_f_list))]] <- Uf
+      U_m_list[[(1+length(U_m_list))]] <- Um
+      H_mat <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
+      H_mat[1,] <- 1
+      H_list2[[(1+length(H_list2))]] <- H_mat
+    }
+    for(age in 1:na){
+      T_f <- T_data_f[[age]]
+      T_m <- T_data_m[[age]]
+      T_f_list[[(1+length(T_f_list))]] <- T_f
+      T_m_list[[(1+length(T_m_list))]] <- T_m
+      F_f <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
+      F_m <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
+      F_f[1,] <- F_list_females[[year]][age,]
+      F_m[1,] <- F_list_males[[year]][age,]
+      F_f_list[[(1+length(F_f_list))]] <- F_f
+      F_m_list[[(1+length(F_m_list))]] <- F_m
+    }
+    ## create the appropriate block-diagonal matrices
+    U_f_BDD <- block_diag_function(U_f_list) ## direct sum of female survivorship, independent over stage (ns diagonal blocks)
+    U_m_BDD <- block_diag_function(U_m_list) ## direct sum of male survivorship, independent over stage (ns diagonal blocks)
+    H_BDD <- block_diag_function(H_list2) ## direct sum of which age newborns enter, independent over stage (ns diagonal blocks)
+    T_f_BDD <- block_diag_function(T_f_list) ## direct sum of female stage transitions, independent over age (na diagonal blocks)
+    T_m_BDD <- block_diag_function(T_m_list) ## direct sum of male stage transitions, independent over age (na diagonal blocks)
+    F_f_BDD <- block_diag_function(F_f_list) ## direct sum of female stage->stage reproductions, independent over age (na blocks)
+    F_m_BDD <- block_diag_function(F_m_list) ## direct sum of male stage->stage reproductions, independent over age (na blocks)
+
+    ## create the appropriate projection matrices
+    U_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
+      U_f_BDD %*%
+      K_perm_mat(ns, na) %*%
+      T_f_BDD
+
+    ## create sex-specific age*stage projections
+    U_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
+      U_m_BDD %*%
+      K_perm_mat(ns, na) %*%
+      T_m_BDD
+
+    F_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
+      H_BDD %*%
+      K_perm_mat(ns, na) %*%
+      F_f_BDD
+
+    F_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
+      H_BDD %*%
+      K_perm_mat(ns, na) %*%
+      F_m_BDD
+
+    ## if year == 1 we are at the boundary condition t=0 apply time-invariant kinship projections
+    if(year == 1){
+      ## Output of the static model
+      kin_out_1 <- all_kin_dy(U_tilde_females,
+                              U_tilde_males ,
+                              F_tilde_females,
+                              F_tilde_males,
+                              1-birth_female,
+                              na,
+                              ns,
+                              parity,
+                              sex_Focal,
+                              initial_stage_Focal)
+      ### Relative lists' first entries
+      Focal_array[[(1+length(Focal_array))]] <- kin_out_1[["Focal"]]
+      daughter_array[[(1+length(daughter_array))]] <- kin_out_1[["d"]]
+      grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out_1[["gd"]]
+      great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out_1[["ggd"]]
+      mom_array[[(1+length(mom_array))]] <- kin_out_1[["m"]]
+      gran_array[[(1+length(gran_array))]] <- kin_out_1[["gm"]]
+      great_gran_array[[(1+length(great_gran_array))]] <- kin_out_1[["ggm"]]
+      younger_sis_array[[( 1+length(younger_sis_array))]] <- kin_out_1[["ys"]]
+      older_sister_array[[(1+length(older_sister_array))]] <- kin_out_1[["os"]]
+      younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out_1[["ya"]]
+      older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out_1[["oa"]]
+      younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out_1[["nys"]]
+      older_niece_array[[(1+length(older_niece_array))]] <- kin_out_1[["nos"]]
+      younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out_1[["cya"]]
+      older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out_1[["coa"]]
+      changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out_1[["ps"]]
+
+    }
+    updating_Focal <- Focal_array[[year]]
+    updating_daughter <- daughter_array[[year]]
+    updating_grand_daughter <- grand_daughter_array[[year]]
+    updating_great_grand_daughter <- great_grand_daughter_array[[year]]
+    updating_mom <- mom_array[[year]]
+    updating_gran <- gran_array[[year]]
+    updating_great_gran <- great_gran_array[[year]]
+    updating_younger_sis <- younger_sis_array[[year]]
+    updating_older_sis <- older_sister_array[[year]]
+    updating_youner_aunt <- younger_aunt_array[[year]]
+    updating_older_aunt <- older_aunt_array[[year]]
+    updating_younger_niece <- younger_niece_array[[year]]
+    updating_older_niece <- older_niece_array[[year]]
+    updating_younger_cousin <- younger_cousin_array[[year]]
+    updating_older_cousin <- older_cousin_array[[year]]
+    updating_pop_struct <- changing_pop_struct[[year]]
+
+    ## Output of the time-variant model
+    kin_out <- all_kin_dy_TV(U_tilde_females,
+                             U_tilde_males,
+                             F_tilde_females,
+                             F_tilde_males,
+                             1-birth_female,
+                             na,
+                             ns,
+                             parity,
+                             sex_Focal,
+                             initial_stage_Focal,
+                             updating_Focal,
+                             updating_daughter,
+                             updating_grand_daughter,
+                             updating_great_grand_daughter,
+                             updating_mom,
+                             updating_gran,
+                             updating_great_gran,
+                             updating_older_sis,
+                             updating_younger_sis,
+                             updating_older_niece,
+                             updating_younger_niece,
+                             updating_older_aunt,
+                             updating_youner_aunt,
+                             updating_older_cousin,
+                             updating_younger_cousin,
+                             updating_pop_struct)
+    ## Relative lists entries correspond to timescale periods (each entry an kin age*stage*2 by Focal age matrix)
+    Focal_array[[(1+length(Focal_array))]] <- kin_out[["Focal"]]
+    daughter_array[[(1+length(daughter_array))]] <- kin_out[["d"]]
+    grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out[["gd"]]
+    great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out[["ggd"]]
+    mom_array[[(1+length(mom_array))]] <- kin_out[["m"]]
+    gran_array[[(1+length(gran_array))]] <- kin_out[["gm"]]
+    great_gran_array[[(1+length(great_gran_array))]] <- kin_out[["ggm"]]
+    younger_sis_array[[(1+length(younger_sis_array))]] <- kin_out[["ys"]]
+    older_sister_array[[(1+length(older_sister_array))]] <- kin_out[["os"]]
+    younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out[["ya"]]
+    older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out[["oa"]]
+    younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out[["nys"]]
+    older_niece_array[[(1+length(older_niece_array))]] <- kin_out[["nos"]]
+    younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out[["cya"]]
+    older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out[["coa"]]
+    changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out[["ps"]]
+    pb$tick()
+  }
+  tictoc::toc()
+  ## create a list of output kin -- each element a time-period specific list of matrices
+  ## label the kin names to match DemoKin:
+  relative_data <- list("Focal" = Focal_array,
+                        "d" = daughter_array,
+                        "gd" = grand_daughter_array,
+                        "ggd" = great_grand_daughter_array,
+                        "m" = mom_array,
+                        "gm" = gran_array,
+                        "ggm" = great_gran_array,
+                        "ys" = younger_sis_array,
+                        "os" = older_sister_array,
+                        "ya" = younger_aunt_array,
+                        "oa" = older_aunt_array,
+                        "nys" = younger_niece_array,
+                        "nos" = older_niece_array,
+                        "cya" = younger_cousin_array,
+                        "coa" = older_cousin_array)
+
+  relative_names <- names(relative_data)
+  ## create a nice data frame output
+  kin_full <- create_full_dists_df(relative_data,
+                                   relative_names,
+                                   output_years,
+                                   output_years[1],
+                                   na,
+                                   ns,
+                                   output_kin)
+  if(summary_kin){
+    kin_summary <- create_cumsum_df(relative_data,
+                                relative_names,
+                                output_years,
+                                output_years[1],
+                                na,
+                                ns,
+                                output_kin)
+    kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)}
+  else{
+    kin_out <- kin_full
+  }
+  return(kin_out)
+}
+
+
+#' Title time invariant two-sex multi-state kin projections
+#'
+#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
+#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
+#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
+#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
+#' @param alpha scalar. birth ratio (male:female)
+#' @param na scalar. number of ages.
+#' @param ns scalar. number of stages.
+#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
+#' @param sex_Focal logical. Female or Male
+#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
+#'
+#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+#' yielding the age*stage distribution of kin for each age of Focal
+
+all_kin_dy <- function(Uf,
+                       Um,
+                       Ff,
+                       Fm,
+                       alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
+                       na, ## na = number of ages
+                       ns, ## ns = number of stages
+                       Parity,
+                       sex_Focal, ## binary "F" or "M"
+                       Initial_stage_Focal){
+
+  n <- nrow(Uf) ## number of ages * stages for each sex
+
+  ## Projection matrices:
+
+  ## Uproj is a block diagonal matrix of block-structured Age*Stage matrices; independently over sex transfers individuals across stage and up age
+  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
+  ## Fproj is a Sex-block-structured matrix of block-structured Age*Stage matrices where males and females BOTH reproduce (by stage)
+  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  Fproj[1:n, 1:n] <- (1-alpha)*Ff ## Ff is Age*Stage block structured giving rate at which females in age-stage produce individuals in age-stage
+  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
+  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm ## Fm is Age*Stage block structured giving rate at which males in age-stage produce individuals in age-stage
+  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
+
+  ## Fprojstar is a Sex-block-structured matrix of block-structured Age*Stage matrices where ONLY females reproduce
+  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
+  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
+  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
+
+  ## The stable population structure is an age*stage*sex vector:
+  ##                                                            1:n gives the female age*stage structure
+  ##                                                            (1+n):2n gives the male age*stage structure
+  population_age_stage_structure <- SD(Uproj + Fprojstar)
+
+  ### Stable distribution of mothers needs adjusting if we work with parity
+  if(Parity){
+    Initial_stage_Focal <- 1
+
+    population_age_stage_of_parenting <- pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+  else{
+    population_age_stage_of_parenting <- pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+
+  ####################################### The dynamics of Kinship, starting with Focal who is no longer a unit vector
+
+  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
+  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
+  m_t <- get_G(Um, na, ns)
+  G_tilde <- block_diag_function(list(f_t,m_t))
+  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  IC_Focal <- rep(0, 2*n)
+  if(sex_Focal == "Female"){
+    entry <- 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1}
+  else{
+    entry <- n + 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1
+  }
+
+  ### empty kin matrices for all of Focal's kin
+  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_aunt_uncle  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+
+
+  ### Initial distributions for kin with non-zero deterministic initial conditions:
+  # Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
+  X_Focal[,1] <- IC_Focal
+  X_parents[, 1] <- parents_joint_age_stage
+
+  ### projection all kin with deterministic initial conditions
+  for(i in 1 : (na-1)){
+    X_Focal[,i+1] <- G_tilde %*% X_Focal[,i]
+    X_parents[, i+1] <- Uproj %*% X_parents[, i]
+    X_younger_sibs[,i+1] <- Uproj %*% X_younger_sibs[,i] + Fprojstar %*% X_parents[,i]
+    X_younger_niece_nephew[,i+1] <- Uproj %*% X_younger_niece_nephew[,i] + Fproj %*% X_younger_sibs[,i]
+    X_children[,i+1] <- Uproj %*% X_children[,i] + Fproj %*% X_Focal[,i]
+    X_grand_children[,i+1] <- Uproj %*% X_grand_children[,i] + Fproj %*% X_children[,i]
+    X_great_grand_children[,i+1] <- Uproj %*% X_great_grand_children[,i] + Fproj %*% X_grand_children[,i]
+  }
+
+  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
+  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
+  IC_f_grand_pars <- mothers_age_dist
+  IC_m_grand_pars <- fathers_age_dist
+  IC_f_great_grand_pars <- mothers_age_dist
+  IC_m_great_grand_pars <- fathers_age_dist
+  IC_older_sibs_f <- mothers_age_dist
+  IC_younger_aunts_uncles_f <- mothers_age_dist
+  IC_younger_aunts_uncles_m <- fathers_age_dist
+  IC_older_niece_nephew_f <- mothers_age_dist
+  for(ic in 1 : (na)){
+    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*X_parents[,ic] ## IC the sum of parents of Focal's parents,
+    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*X_grand_parents[,ic]
+    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*X_children[,ic]
+    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*X_grand_children[,ic]
+    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*X_younger_sibs[,ic]
+  }
+
+  ### Projections of grand parenst, older sibs, younger aunts/uncles, older nieces/nephews
+  for(i in 1: (na-1)){
+    X_grand_parents[, i+1] <- Uproj %*% X_grand_parents[, i]
+    X_great_grand_parents[, i+1] <- Uproj %*% X_great_grand_parents[, i]
+    X_older_sibs[,i+1] <- Uproj %*% X_older_sibs[,i]
+    X_older_niece_nephew[,i+1] <- Uproj %*% X_older_niece_nephew[,i] + Fproj %*% X_older_sibs[,i]
+    X_younger_aunts_uncles[,i+1] <- Uproj %*% X_younger_aunts_uncles[,i] + Fprojstar %*% X_grand_parents[,i]
+  }
+
+  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
+  ## older unts/uncles, older cousins, younger cousins
+  IC_older_aunt_uncle_f <- mothers_age_dist
+  IC_older_aunt_uncle_m <- fathers_age_dist
+  IC_older_cousins_f <- mothers_age_dist
+  IC_older_cousins_m <- fathers_age_dist
+  IC_younger_cousins_f <- mothers_age_dist
+  IC_younger_cousins_m <- fathers_age_dist
+  for(ic in 1 : (na-1)){
+    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*X_older_sibs[,ic]
+    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*X_older_niece_nephew[,ic]
+    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*X_younger_niece_nephew[,ic]
+  }
+
+  ## Projections of older unts/uncles, older cousins, younger cousins
+  for(i in 1: (na-1)){
+    X_older_aunt_uncle[,i+1] <- Uproj %*% X_older_aunt_uncle[,i]
+    X_older_cousins[,i+1] <- Uproj %*% X_older_cousins[,i] + Fproj %*% X_older_aunt_uncle[,i]
+    X_younger_cousins[,i+1] <- Uproj %*% X_younger_cousins[,i] + Fproj %*% X_younger_aunts_uncles[,i]
+  }
+
+  #### OUTPUT of all kin
+  return(list("Focal" = X_Focal,
+              "d" = X_children,
+              "gd" = X_grand_children,
+              "ggd" = X_great_grand_children,
+              "m" = X_parents,
+              "gm" = X_grand_parents,
+              "ggm" = X_great_grand_parents,
+              "os" = X_older_sibs,
+              "ys" = X_younger_sibs,
+              "nos" = X_older_niece_nephew,
+              "nys" = X_younger_niece_nephew,
+              "oa" = X_older_aunt_uncle,
+              "ya" = X_younger_aunts_uncles,
+              "coa" = X_older_cousins,
+              "cya" = X_younger_cousins,
+              "ps" = population_age_stage_structure
+  ))
+}
+
+
+#' Title time-variant two-sex multi-state kin projections
+#'
+#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
+#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
+#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
+#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
+#' @param alpha scalar. birth ratio (male:female)
+#' @param na scalar. number of ages.
+#' @param ns scalar. number of stages.
+#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
+#' @param sex_Focal logical. Female or Male
+#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
+#' @param previous_kin_Focal matrix. last years kinship output.
+#' @param prev_kin_children matrix. last years kinship output.
+#' @param prev_kin_grandchildren matrix. last years kinship output.
+#' @param prev_kin_greatgrandchildren matrix. last years kinship output.
+#' @param prev_kin_parents matrix. last years kinship output.
+#' @param prev_kin_grand_parents matrix. last years kinship output.
+#' @param prev_kin_older_sibs matrix. last years kinship output.
+#' @param prev_kin_younger_sibs matrix. last years kinship output.
+#' @param prev_kin_older_niece_nephew matrix. last years kinship output.
+#' @param prev_kin_younger_niece_nephew matrix. last years kinship output.
+#' @param prev_kin_older_aunts_uncles matrix. last years kinship output.
+#' @param prev_kin_younger_aunts_uncles matrix. last years kinship output.
+#' @param prev_kin_older_cousins matrix. last years kinship output.
+#' @param prev_kin_younger_cousins matrix. last years kinship output.
+#' @param previous_population_age_stage_structure vector. The transient "population structure" (age*stage distributed)
+#'
+#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
+#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
+#' yielding the age*stage distribution of kin for each age of Focal
+#'
+all_kin_dy_TV <- function(Uf,
+                          Um,
+                          Ff,
+                          Fm,
+                          alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
+                          na, ## number of ages
+                          ns, ## number of stages
+                          Parity,
+                          sex_Focal,
+                          Initial_stage_Focal,
+                          previous_kin_Focal,
+                          prev_kin_children,
+                          prev_kin_grandchildren,
+                          prev_kin_greatgrandchildren,
+                          prev_kin_parents,
+                          prev_kin_grand_parents,
+                          prev_kin_great_grand_parents,
+                          prev_kin_older_sibs,
+                          prev_kin_younger_sibs,
+                          prev_kin_older_niece_nephew,
+                          prev_kin_younger_niece_nephew,
+                          prev_kin_older_aunts_uncles,
+                          prev_kin_younger_aunts_uncles,
+                          prev_kin_older_cousins,
+                          prev_kin_younger_cousins,
+                          previous_population_age_stage_structure){
+
+  n <- nrow(Uf)
+  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
+  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  Fproj[1:n, 1:n] <- (1-alpha)*Ff
+  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
+  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm
+  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
+  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
+  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
+  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
+
+  population_age_stage_structure <- previous_population_age_stage_structure
+  population_age_stage_structure <- population_age_stage_structure/sum(population_age_stage_structure)
+  population_age_stage_structure_next <- (Uproj + Fprojstar)%*%population_age_stage_structure
+
+  ### Stable distribution of mothers needs adjusting if we work with parity
+  if(Parity){
+    Initial_stage_Focal <- 1
+
+    population_age_stage_of_parenting <- pi_mix_TV_parity(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+  else{
+
+    population_age_stage_of_parenting <- pi_mix_TV(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
+    mothers_age_stage <- population_age_stage_of_parenting[[2]]
+    fathers_age_stage <- population_age_stage_of_parenting[[3]]
+
+    mothers_age_dist <- population_age_stage_of_parenting[[4]]
+    fathers_age_dist <- population_age_stage_of_parenting[[5]]
+
+  }
+
+  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
+  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
+  m_t <- get_G(Um, na, ns)
+  G_tilde <- block_diag_function(list(f_t,m_t))
+  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  IC_Focal <- rep(0, 2*n)
+  if(sex_Focal == "Female"){
+    entry <- 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1}
+  else{
+    entry <- n + 1 + (Initial_stage_Focal-1)*na
+    IC_Focal[entry] <- 1
+  }
+
+  ### empty kin matrices for all of Focal's kin
+  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_aunt_uncle <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_older_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+  X_younger_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
+
+  ### Initial distributions for kin with non-zero deterministic initial conditions:
+  ## Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
+  X_Focal[,1] <- IC_Focal
+  X_parents[, 1] <- parents_joint_age_stage
+  ### projection all above kin with deterministic initial conditions
+  for(i in 1 : (na-1)){
+    X_Focal[,i+1] <- G_tilde %*% previous_kin_Focal[,i]
+    X_parents[, i+1] <- Uproj %*% prev_kin_parents[, i]
+    X_younger_sibs[,i+1] <- Uproj %*% prev_kin_younger_sibs[,i] + Fprojstar %*% prev_kin_parents[,i]
+    X_younger_niece_nephew[,i+1] <- Uproj %*% prev_kin_younger_niece_nephew[,i] + Fproj %*% prev_kin_younger_sibs[,i]
+    X_children[,i+1] <- Uproj %*% prev_kin_children[,i] + Fproj %*% previous_kin_Focal[,i]
+    X_grand_children[,i+1] <- Uproj %*% prev_kin_grandchildren[,i] + Fproj %*% prev_kin_children[,i]
+    X_great_grand_children[,i+1] <- Uproj %*% prev_kin_greatgrandchildren[,i] + Fproj %*% prev_kin_grandchildren[,i]
+  }
+
+  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
+  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
+  IC_f_grand_pars <- mothers_age_dist
+  IC_m_grand_pars <- fathers_age_dist
+  IC_f_great_grand_pars <- mothers_age_dist
+  IC_m_great_grand_pars <- fathers_age_dist
+  IC_younger_aunts_uncles_f <- mothers_age_dist
+  IC_younger_aunts_uncles_m <- fathers_age_dist
+  IC_older_sibs_f <- mothers_age_dist
+  IC_older_niece_nephew_f <- mothers_age_dist
+  for(ic in 1 : (na)){
+    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*prev_kin_parents[,ic] ## IC the sum of parents of Focal's parents,
+    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*prev_kin_grand_parents[,ic]
+    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*prev_kin_children[,ic]
+    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*prev_kin_grandchildren[,ic]
+    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*prev_kin_younger_sibs[,ic]
+  }
+
+  ### Projections of older sibs, younger aunts/uncles, older nieces/nephews
+  for(i in 1: (na-1)){
+    X_grand_parents[, i+1] <- Uproj %*% prev_kin_grand_parents[, i]
+    X_great_grand_parents[, i+1] <- Uproj %*% prev_kin_great_grand_parents[, i]
+    X_older_sibs[,i+1] <- Uproj %*% prev_kin_older_sibs[,i]
+    X_older_niece_nephew[,i+1] <- Uproj %*% prev_kin_older_niece_nephew[,i] + Fproj %*% prev_kin_older_sibs[,i]
+    X_younger_aunts_uncles[,i+1] <- Uproj %*% prev_kin_younger_aunts_uncles[,i] + Fprojstar %*% prev_kin_grand_parents[,i]
+  }
+
+  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
+  ## older unts/uncles, older cousins, younger cousins
+  IC_older_aunt_uncle_f <- mothers_age_dist
+  IC_older_aunt_uncle_m <- fathers_age_dist
+  IC_older_cousins_f <- mothers_age_dist
+  IC_older_cousins_m <- fathers_age_dist
+  IC_younger_cousins_f <- mothers_age_dist
+  IC_younger_cousins_m <- fathers_age_dist
+  for(ic in 1 : (na-1)){
+    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*prev_kin_older_sibs[,ic]
+    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*prev_kin_older_niece_nephew[,ic]
+    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*prev_kin_younger_niece_nephew[,ic]
+  }
+
+  ## Projections of older unts/uncles, older cousins, younger cousins
+  for(i in 1: (na-1)){
+    X_older_aunt_uncle[,i+1] <- Uproj %*% prev_kin_older_aunts_uncles[,i]
+    X_older_cousins[,i+1] <- Uproj %*% prev_kin_older_cousins[,i] + Fproj %*% prev_kin_older_aunts_uncles[,i]
+    X_younger_cousins[,i+1] <- Uproj %*% prev_kin_younger_cousins[,i] + Fproj %*% prev_kin_younger_aunts_uncles[,i]
+  }
+
+  return(list("Focal" = X_Focal,
+              "d" = X_children,
+              "gd" = X_grand_children,
+              "ggd" = X_great_grand_children,
+              "m" = X_parents,
+              "gm" = X_grand_parents,
+              "ggm" = X_great_grand_parents,
+              "os" = X_older_sibs,
+              "ys" = X_younger_sibs,
+              "nos" = X_older_niece_nephew,
+              "nys" = X_younger_niece_nephew,
+              "oa" = X_older_aunt_uncle,
+              "ya" = X_younger_aunts_uncles,
+              "coa" = X_older_cousins,
+              "cya" = X_younger_cousins,
+              "ps" = population_age_stage_structure_next))
+}
+
+################## Create data frame output
+
+## Use of "pipe" (don't understand the name, but hey)
+`%>%` <- magrittr::`%>%`
+
+#' Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
+#'
+#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
+#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
+#' @param years vector. The timescale on which we implement the kinship model.
+#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
+#' @param na numeric. Number of ages.
+#' @param ns numeric. Number of stages.
+#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
+#'
+#' @return A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
+#'
+create_cumsum_df <- function(kin_matrix_lists,
+                             kin_names,
+                             years,
+                             start_year,
+                             na,
+                             ns,
+                             specific_kin = NULL){
+  df_year_list <- list()
+  for(j in years){
+    ii <- as.numeric(j) - start_year + 1
+    df_list <- list()
+    for(i in 1 : length(kin_names)){
+      kin_member <- kin_names[[i]]
+      kin_data <- kin_matrix_lists[[i]]
+      kin_data <- kin_data[[ii]]
+      df <- as.data.frame(as.matrix(kin_data))
+      dims <- dim( kin_data)
+      nr <- dims[1]
+      nc <- dims[2]
+      female_kin <- df[1:(nr/2), 1:nc]
+      male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      female_kin$stage <- rep(seq(1, ns), na)
+      male_kin$stage <- rep(seq(1, ns), na)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
+      female_kin$Sex <- "Female"
+      male_kin$Sex <- "Male"
+      both_kin <- rbind(female_kin, male_kin)
+      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
+        dplyr::group_by(variable, stage, Sex) %>%
+        dplyr::summarise(num = sum(value)) %>%
+        dplyr::ungroup()
+      both_kin <- both_kin %>% dplyr::transmute(age_focal = variable,
+                                                stage_kin = as.factor(stage),
+                                                count = num,
+                                                sex_kin = Sex)
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal)) - 1
+      df <- both_kin
+      df$year <- j
+      df$group <- kin_member
+      df_list[[length(df_list)+1]] <- df
+    }
+    df_list <- do.call("rbind", df_list)
+    df_year_list[[(1+length(df_year_list))]] <- df_list
+  }
+  df_year_list <- do.call("rbind", df_year_list)
+  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
+                                                 cohort_factor = as.factor(cohort))
+  if(!is.null(specific_kin)){
+    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
+  }
+  return(df_year_list)
+}
+
+#' Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
+#'
+#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
+#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
+#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
+#' @param years vector. The timescale on which we implement the kinship model.
+#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
+#' @param na numeric. Number of ages.
+#' @param ns numeric. Number of stages.
+#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
+#'
+#' @return A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
+#'
+create_full_dists_df <- function(kin_matrix_lists,
+                                 kin_names,
+                                 years,
+                                 start_year,
+                                 na,
+                                 ns,
+                                 specific_kin = NULL){
+  df_year_list <- list()
+  for(j in years){
+    ii <- as.numeric(j) - start_year + 1
+    df_list <- list()
+    for(i in 1 : length(kin_names)){
+      kin_member <- kin_names[[i]]
+      kin_data <- kin_matrix_lists[[i]]
+      kin_data <- kin_data[[ii]]
+      df <- as.data.frame(as.matrix(kin_data))
+      dims <- dim( kin_data)
+      nr <- dims[1]
+      nc <- dims[2]
+      female_kin <- df[1:(nr/2), 1:nc]
+      male_kin <- df[ (1+nr/2) : nr, 1:nc]
+      female_kin$stage <- rep(seq(1, ns), na)
+      male_kin$stage <- rep(seq(1, ns), na)
+      female_kin$age <- rep(seq(0, (na-1)), each = ns)
+      male_kin$age <- rep(seq(0, (na-1)), each = ns)
+      female_kin$Sex <- "Female"
+      male_kin$Sex <- "Male"
+      both_kin <- rbind(female_kin, male_kin)
+      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
+        dplyr::transmute(age_focal = variable,
+                         age_kin = age,
+                         stage_kin = as.factor(stage),
+                         count = value,
+                         sex_kin = Sex)
+      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal))-1
+      df <- both_kin
+      df$year <- j
+      df$group <- kin_member
+      df_list[[length(df_list)+1]] <- df
+    }
+    df_list <- do.call("rbind", df_list)
+    df_year_list[[(1+length(df_year_list))]] <- df_list
+  }
+  df_year_list <- do.call("rbind", df_year_list)
+  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
+                                                 cohort_factor = as.factor(cohort))
+  if(!is.null(specific_kin)){
+    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
+  }
+  return(df_year_list)
+}
+
+
+
+## Construct a matrix composed as a direct sum of a list of matrices
+block_diag_function <- function(mat_list){
+  s = length(mat_list)
+  u1 = mat_list[[1]]
+  dims <- dim(u1)
+  r = dims[1]
+  diagmat <- Matrix::Matrix(nrow = (r*s), ncol = (r*s), data = 0, sparse = TRUE)
+  for(i in 1:s){
+    diagmat = diagmat + kronecker(E_matrix(i,i,s,s), mat_list[[i]])
+  }
+  return(diagmat)
+}
+
+## Construct a matrix which transfers Focal across stages, while ensuring Focal survives with probability 1
+get_G <- function(U, na, ns){
+  sig <- Matrix::t(rep(1,na*ns)) %*% U
+  diag <- Matrix::diag(sig[1,])
+  G <- U %*% MASS::ginv(diag)
+  return(G)
+}
+
+#' Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix <- function(Uf, Um, Ff, Fm, alpha, na, ns){
+  n <- length(Uf[1,])
+  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  F_block[1:n, 1:n] <- (1-alpha)*Ff
+  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
+  A <- block_diag_function(list(Uf,Um)) + F_block
+  stable_dist_vec <- SD(A)
+  ### Joint distributions
+  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  ### Age distributions
+  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
+  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
+  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+#' Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
+#'
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_TV <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
+  n <- length(Ff[1,])
+  ### Joint distributions
+  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  ### Age distributions
+  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
+  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
+  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+#' Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_parity <- function(Uf, Um, Ff, Fm, alpha, na, ns){
+  n <- length(Uf[1,])
+  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
+  F_block[1:n, 1:n] <- (1-alpha)*Ff
+  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
+  A <- block_diag_function(list(Uf,Um)) + F_block
+  stable_dist_vec <- SD(A)
+  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  m_mat <- pi_f %*% Matrix::t(rep(1,na))
+  d_mat <- pi_m %*% Matrix::t(rep(1,na))
+  pi_F <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
+  pi_M <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
+  for(i in 1:na){
+    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
+    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
+  }
+  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
+  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
+  ### Joint distributions
+  pi_f <- out_mum %*% pi_F
+  pi_m <- out_dad %*% pi_M
+  return(list(rbind(pi_f,pi_m), pi_f, pi_m, pi_F, pi_M))
+}
+
+#' Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
+#'
+#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
+#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
+#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
+#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
+#' @param alpha scalar. Birth ratio male:female
+#' @param na scalar. Number of age-classes
+#' @param ns scalar. Number of stages
+#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
+#'
+#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
+#'                            list[[2]] = female age*stage distribution normalised
+#'                            list[[3]] = male age*stage distribution normalised
+#'                            list[[4]] = female marginal age distribution normalised
+#'                            list[[5]] = male marginal age distribution normalised
+#'
+pi_mix_TV_parity <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
+  n <- length(Ff[1,])
+  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
+  pi_f <- pi_f / abs(sum(pi_f))
+  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
+  pi_m <- pi_m / abs(sum(pi_m))
+  m_mat <- pi_f %*% Matrix::t(rep(1,na))
+  d_mat <- pi_m %*% Matrix::t(rep(1,na))
+  pi_F  <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
+  pi_M <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
+  for(i in 1:na){
+    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
+    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
+  }
+  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
+  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
+  ### Joint distributions
+  pi_f <- out_mum %*% pi_F
+  pi_m <- out_dad %*% pi_M
+  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+}
+
+
+######################################################### Some useful utility functions required
+
+
+###################################################### Eigen-decomposition of a matrix
+
+# Calculate the spectral radius of a matrix (growth rate in Demographics)
+lambda <- function(PM) {
+  lead_eig <- (abs(eigen(PM, only.values = TRUE)$values))
+  lead_eig <- lead_eig[which.max(lead_eig)]
+  return(lead_eig)
+}
+# Find the column-eigenvector corresponding to the spectral radius (Stable population structure in Demographics)
+SD <- function(PM) {
+  spectral_stuff <- eigen(PM)
+  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
+  # normalise...
+  vec_lambda <- spectral_stuff/sum(spectral_stuff)
+  return(vec_lambda)
+}
+# Find the row-eigenvector corresponding to the spectral radius (Stable reproductive values in Demographics)
+RD <- function(PM) {
+  spectral_stuff <- eigen(t(PM))
+  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
+  # normalise...
+  vec_lambda <- spectral_stuff/sum(spectral_stuff)
+  return(vec_lambda)
+}
+
+###################################################### Useful matrix operations
+
+## Constructing a unit vector with a 1 in the ith position
+e_vector <- function(i, n){
+  e <- rep(0, n)
+  e[i] <- 1
+  return(e)
+}
+## Creating a matrix of zeros with a 1 in the i,j-th entry
+E_matrix <- function(i,j,n,m){
+  E <- Matrix::Matrix(nrow = (n), ncol = (m), data = 0, sparse = TRUE)
+  E[i,j] <- 1
+  return(E)
+
+}
+## Creating the Vec-commutation matrix
+K_perm_mat <- function(n,m){
+  perm <- Matrix::Matrix(nrow = (n*m), ncol = (n*m), data = 0, sparse = TRUE)
+  for(i in 1:n){
+    for(j in 1:m){
+      perm = perm + kronecker( E_matrix(i,j,n,m) , Matrix::t(E_matrix(i,j,n,m)) )
+    }
+  }
+  return(perm)
+}
+
+
+
+
+
+
+
+

From 932f26e61766d94fa356d32253fb708170f98024 Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Tue, 3 Jun 2025 11:50:08 +0100
Subject: [PATCH 86/89] Update kin_multi_stage_time_variant_2sex.R

Updating Focal's parents 2-dimensional age*stage distribution at birth. Previous code repeated mother's dist. for father.

Joe error
---
 R/kin_multi_stage_time_variant_2sex.R | 25 ++++++++++++++++++-------
 1 file changed, 18 insertions(+), 7 deletions(-)

diff --git a/R/kin_multi_stage_time_variant_2sex.R b/R/kin_multi_stage_time_variant_2sex.R
index f52d3fa..6b51be4 100644
--- a/R/kin_multi_stage_time_variant_2sex.R
+++ b/R/kin_multi_stage_time_variant_2sex.R
@@ -1,5 +1,4 @@
 
-
 #' Estimate kin counts by age, stage, and sex, in a time variant framework
 
 #' @description Implementation of combined formal demographic models: Caswell II,III,IV.
@@ -339,6 +338,9 @@ all_kin_dy <- function(Uf,
     Initial_stage_Focal <- 1
 
     population_age_stage_of_parenting <- pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
     mothers_age_stage <- population_age_stage_of_parenting[[2]]
     fathers_age_stage <- population_age_stage_of_parenting[[3]]
 
@@ -348,6 +350,9 @@ all_kin_dy <- function(Uf,
   }
   else{
     population_age_stage_of_parenting <- pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
     mothers_age_stage <- population_age_stage_of_parenting[[2]]
     fathers_age_stage <- population_age_stage_of_parenting[[3]]
 
@@ -392,7 +397,7 @@ all_kin_dy <- function(Uf,
   ### Initial distributions for kin with non-zero deterministic initial conditions:
   # Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
   X_Focal[,1] <- IC_Focal
-  X_parents[, 1] <- mothers_age_stage
+  X_parents[, 1] <- parents_joint_age_stage
 
   ### projection all kin with deterministic initial conditions
   for(i in 1 : (na-1)){
@@ -553,6 +558,9 @@ all_kin_dy_TV <- function(Uf,
     Initial_stage_Focal <- 1
 
     population_age_stage_of_parenting <- pi_mix_TV_parity(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
     mothers_age_stage <- population_age_stage_of_parenting[[2]]
     fathers_age_stage <- population_age_stage_of_parenting[[3]]
 
@@ -563,6 +571,9 @@ all_kin_dy_TV <- function(Uf,
   else{
 
     population_age_stage_of_parenting <- pi_mix_TV(Ff, Fm, alpha, na, ns, population_age_stage_structure)
+
+    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
+
     mothers_age_stage <- population_age_stage_of_parenting[[2]]
     fathers_age_stage <- population_age_stage_of_parenting[[3]]
 
@@ -604,7 +615,7 @@ all_kin_dy_TV <- function(Uf,
   ### Initial distributions for kin with non-zero deterministic initial conditions:
   ## Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
   X_Focal[,1] <- IC_Focal
-  X_parents[, 1] <- mothers_age_stage
+  X_parents[, 1] <- parents_joint_age_stage
   ### projection all above kin with deterministic initial conditions
   for(i in 1 : (na-1)){
     X_Focal[,i+1] <- G_tilde %*% previous_kin_Focal[,i]
@@ -873,7 +884,7 @@ pi_mix <- function(Uf, Um, Ff, Fm, alpha, na, ns){
   ### Age distributions
   pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
   pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
-  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
 }
 
 #' Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
@@ -901,7 +912,7 @@ pi_mix_TV <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
   ### Age distributions
   pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
   pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
-  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
 }
 
 #' Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
@@ -944,7 +955,7 @@ pi_mix_parity <- function(Uf, Um, Ff, Fm, alpha, na, ns){
   ### Joint distributions
   pi_f <- out_mum %*% pi_F
   pi_m <- out_dad %*% pi_M
-  return(list(c(pi_f,pi_m), pi_f, pi_m, pi_F, pi_M))
+  return(list(rbind(pi_f,pi_m), pi_f, pi_m, pi_F, pi_M))
 }
 
 #' Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
@@ -983,7 +994,7 @@ pi_mix_TV_parity <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
   ### Joint distributions
   pi_f <- out_mum %*% pi_F
   pi_m <- out_dad %*% pi_M
-  return(list(c(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
+  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
 }
 
 

From 7481930ca7b29957f180100416ed6527e1a7aa3b Mon Sep 17 00:00:00 2001
From: ButterickJoe <135118165+ButterickJoe@users.noreply.github.com>
Date: Tue, 3 Jun 2025 11:52:47 +0100
Subject: [PATCH 87/89] Delete kin_multi_stage_time_variant_2sex.R

removing wrongly placed file
---
 kin_multi_stage_time_variant_2sex.R | 1061 ---------------------------
 1 file changed, 1061 deletions(-)
 delete mode 100644 kin_multi_stage_time_variant_2sex.R

diff --git a/kin_multi_stage_time_variant_2sex.R b/kin_multi_stage_time_variant_2sex.R
deleted file mode 100644
index 6b51be4..0000000
--- a/kin_multi_stage_time_variant_2sex.R
+++ /dev/null
@@ -1,1061 +0,0 @@
-
-#' Estimate kin counts by age, stage, and sex, in a time variant framework
-
-#' @description Implementation of combined formal demographic models: Caswell II,III,IV.
-
-#' @param U_list_females list with matrix entries: period-specific female survival probabilities. Age in rows and states in columns.
-#' @param U_list_males list with matrix entries: period-specific male survival probabilities. Age in rows and states in columns.
-#' @param F_list_females list with matrix with elements: period-specific female fertility (age in rows and states in columns).
-#' @param F_list_males list with matrix entries: period-specific male fertility (age in rows and states in columns).
-#' @param T_list_females list of lists with matrix entries: each outer list entry is period-specific, and composed of
-#'                     a list of stochastic matrices which describe age-specific female probabilities of transferring stage
-#' @param T_list_males list of lists with matrix entries: each outer list entry is period-specific, and composed of
-#'                     a list of stochastic matrices which describe age-specific male probabilities of transferring stage
-#' @param H_list list with matrix entries: redistribution of newborns across each stage to a specific age-class
-#' @param birth_female numeric. birth ratio of females to males in population
-#' @param parity logical. parity states imply age distribution of mothers re-scaled to not have parity 0 when Focal born. Default `TRUE`.
-#' @param output_kin vector. A vector of particular kin one wishes to obtain results for, e.g., c("m","d","oa"). Default is all kin types.
-#' @param summary_kin logical. Results as a data frame of accumulated kin by age of Focal if TRUE, and kin by their age*stage distribution by age of Focal if FALSE.
-#' @param sex_Focal character. Female or Male as the user requests.
-#' @param initial_stage_Focal Numeric in Natural number set {1,2,...,}. The stage which Focal is born into (e.g., 1 for parity 0)
-#' @param output_years vector. The times at which we wish to count kin: start year = output_years[1], and end year = output_years[length.]
-#'
-#' @return A data frame with focal age, kin age, kin stage, kin sex, year, cohort, and expected number of kin given these restrictions.
-
-#' @export
-#'
-kin_multi_stage_time_variant_2sex <- function(U_list_females = NULL,
-                                              U_list_males = NULL,
-                                              F_list_females = NULL,
-                                              F_list_males = NULL,
-                                              T_list_females = NULL,
-                                              T_list_males = NULL,
-                                              H_list = NULL,
-                                              birth_female = 0.49, ## Sex ratio -- note is 1 - alpha
-                                              parity = FALSE,
-                                              output_kin = NULL, # enter a vector of specific kin if we only want to analyse these (e.g., c("m","d"))
-                                              summary_kin = TRUE, # Set to FALSE if we want only a full age*stage distribution of kin
-                                              sex_Focal = "Female", # Female Focal is default
-                                              initial_stage_Focal = NULL,
-                                              output_years){
-
-  no_years <- length(U_list_females)
-  na <- nrow(U_list_females[[1]])
-  ns <- ncol(U_list_females[[1]])
-
-  # Ensure inputs are lists of matrices and that the timescale same length
-  # if(length(U_list_females)!=length(output_years)){stop("Timescale inconsistancy")} ## this is due to my struggles with counting! ( e.g., seq(10, 20, 1) != list(1 : 10) )
-  if(!is.list(U_list_females) | !is.list(U_list_males)){stop("U's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
-  if(!is.list(F_list_females) | !is.list(F_list_males)){stop("F's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
-  if(!is.list(T_list_females) | !is.list(T_list_males)){stop("T's must be a list with time-series length. Each list entry should be an age*stage dimensional matrix")}
-
-  ### Define empty lists for the accumulated kin of Focals's life-course -- each list entry will reflect a time-period
-  changing_pop_struct <- list()
-  Focal_array <- list()
-  mom_array <- list()
-  gran_array <- list()
-  great_gran_array <- list()
-  daughter_array <- list()
-  younger_sis_array <- list()
-  grand_daughter_array <-list()
-  great_grand_daughter_array <- list()
-  older_sister_array <- list()
-  younger_aunt_array <- list()
-  older_aunt_array <- list()
-  younger_niece_array <- list()
-  older_niece_array <- list()
-  younger_cousin_array <- list()
-  older_cousin_array <- list()
-
-  ### At each time-period we: 1) -- construct the time-variant projection matrices:
-  ###                               U_tilde : transfers across stage and advances age
-  ###                               F_tilde : makes newborns from stage/age; puts them to stage/age
-  ###                         2) -- project Focal and kin using above projection matrices
-
-  pb <- progress::progress_bar$new(
-    format = "Timescale [:bar] :percent",
-    total = no_years + 1, clear = FALSE, width = 60)
-  tictoc::tic()
-  for(year in 1:no_years){
-    T_data_f <- T_list_females[[year]] ## For each year we have na number of Transfer matrices
-    T_data_m <- T_list_males[[year]]   ## which give probabilities of age-dep movement from stage to stage
-    T_f_list <- list()
-    T_m_list <- list()
-    F_f_list <- list()
-    F_m_list <- list()
-    U_f_list <- list()
-    U_m_list <- list()
-    H_list2 <- list()
-
-    for(stage in 1:ns){
-      Uf <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
-      Matrix::diag(Uf[-1,-ncol(Uf)]) <- U_list_females[[year]][1:(na-1),stage]
-      Uf[na,na] <- U_list_females[[year]][na,stage]
-      Um <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
-      Matrix::diag(Um[-1,-ncol(Um)]) <- U_list_males[[year]][1:(na-1),stage]
-      Um[na,na] <- U_list_males[[year]][na,stage]
-      U_f_list[[(1+length(U_f_list))]] <- Uf
-      U_m_list[[(1+length(U_m_list))]] <- Um
-      H_mat <- Matrix::Matrix(nrow = na, ncol = na, data = 0, sparse = TRUE)
-      H_mat[1,] <- 1
-      H_list2[[(1+length(H_list2))]] <- H_mat
-    }
-    for(age in 1:na){
-      T_f <- T_data_f[[age]]
-      T_m <- T_data_m[[age]]
-      T_f_list[[(1+length(T_f_list))]] <- T_f
-      T_m_list[[(1+length(T_m_list))]] <- T_m
-      F_f <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
-      F_m <- Matrix::Matrix(nrow = ns, ncol = ns, data = 0, sparse = TRUE)
-      F_f[1,] <- F_list_females[[year]][age,]
-      F_m[1,] <- F_list_males[[year]][age,]
-      F_f_list[[(1+length(F_f_list))]] <- F_f
-      F_m_list[[(1+length(F_m_list))]] <- F_m
-    }
-    ## create the appropriate block-diagonal matrices
-    U_f_BDD <- block_diag_function(U_f_list) ## direct sum of female survivorship, independent over stage (ns diagonal blocks)
-    U_m_BDD <- block_diag_function(U_m_list) ## direct sum of male survivorship, independent over stage (ns diagonal blocks)
-    H_BDD <- block_diag_function(H_list2) ## direct sum of which age newborns enter, independent over stage (ns diagonal blocks)
-    T_f_BDD <- block_diag_function(T_f_list) ## direct sum of female stage transitions, independent over age (na diagonal blocks)
-    T_m_BDD <- block_diag_function(T_m_list) ## direct sum of male stage transitions, independent over age (na diagonal blocks)
-    F_f_BDD <- block_diag_function(F_f_list) ## direct sum of female stage->stage reproductions, independent over age (na blocks)
-    F_m_BDD <- block_diag_function(F_m_list) ## direct sum of male stage->stage reproductions, independent over age (na blocks)
-
-    ## create the appropriate projection matrices
-    U_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
-      U_f_BDD %*%
-      K_perm_mat(ns, na) %*%
-      T_f_BDD
-
-    ## create sex-specific age*stage projections
-    U_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
-      U_m_BDD %*%
-      K_perm_mat(ns, na) %*%
-      T_m_BDD
-
-    F_tilde_females <- Matrix::t(K_perm_mat(ns, na)) %*%
-      H_BDD %*%
-      K_perm_mat(ns, na) %*%
-      F_f_BDD
-
-    F_tilde_males <- Matrix::t(K_perm_mat(ns, na)) %*%
-      H_BDD %*%
-      K_perm_mat(ns, na) %*%
-      F_m_BDD
-
-    ## if year == 1 we are at the boundary condition t=0 apply time-invariant kinship projections
-    if(year == 1){
-      ## Output of the static model
-      kin_out_1 <- all_kin_dy(U_tilde_females,
-                              U_tilde_males ,
-                              F_tilde_females,
-                              F_tilde_males,
-                              1-birth_female,
-                              na,
-                              ns,
-                              parity,
-                              sex_Focal,
-                              initial_stage_Focal)
-      ### Relative lists' first entries
-      Focal_array[[(1+length(Focal_array))]] <- kin_out_1[["Focal"]]
-      daughter_array[[(1+length(daughter_array))]] <- kin_out_1[["d"]]
-      grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out_1[["gd"]]
-      great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out_1[["ggd"]]
-      mom_array[[(1+length(mom_array))]] <- kin_out_1[["m"]]
-      gran_array[[(1+length(gran_array))]] <- kin_out_1[["gm"]]
-      great_gran_array[[(1+length(great_gran_array))]] <- kin_out_1[["ggm"]]
-      younger_sis_array[[( 1+length(younger_sis_array))]] <- kin_out_1[["ys"]]
-      older_sister_array[[(1+length(older_sister_array))]] <- kin_out_1[["os"]]
-      younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out_1[["ya"]]
-      older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out_1[["oa"]]
-      younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out_1[["nys"]]
-      older_niece_array[[(1+length(older_niece_array))]] <- kin_out_1[["nos"]]
-      younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out_1[["cya"]]
-      older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out_1[["coa"]]
-      changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out_1[["ps"]]
-
-    }
-    updating_Focal <- Focal_array[[year]]
-    updating_daughter <- daughter_array[[year]]
-    updating_grand_daughter <- grand_daughter_array[[year]]
-    updating_great_grand_daughter <- great_grand_daughter_array[[year]]
-    updating_mom <- mom_array[[year]]
-    updating_gran <- gran_array[[year]]
-    updating_great_gran <- great_gran_array[[year]]
-    updating_younger_sis <- younger_sis_array[[year]]
-    updating_older_sis <- older_sister_array[[year]]
-    updating_youner_aunt <- younger_aunt_array[[year]]
-    updating_older_aunt <- older_aunt_array[[year]]
-    updating_younger_niece <- younger_niece_array[[year]]
-    updating_older_niece <- older_niece_array[[year]]
-    updating_younger_cousin <- younger_cousin_array[[year]]
-    updating_older_cousin <- older_cousin_array[[year]]
-    updating_pop_struct <- changing_pop_struct[[year]]
-
-    ## Output of the time-variant model
-    kin_out <- all_kin_dy_TV(U_tilde_females,
-                             U_tilde_males,
-                             F_tilde_females,
-                             F_tilde_males,
-                             1-birth_female,
-                             na,
-                             ns,
-                             parity,
-                             sex_Focal,
-                             initial_stage_Focal,
-                             updating_Focal,
-                             updating_daughter,
-                             updating_grand_daughter,
-                             updating_great_grand_daughter,
-                             updating_mom,
-                             updating_gran,
-                             updating_great_gran,
-                             updating_older_sis,
-                             updating_younger_sis,
-                             updating_older_niece,
-                             updating_younger_niece,
-                             updating_older_aunt,
-                             updating_youner_aunt,
-                             updating_older_cousin,
-                             updating_younger_cousin,
-                             updating_pop_struct)
-    ## Relative lists entries correspond to timescale periods (each entry an kin age*stage*2 by Focal age matrix)
-    Focal_array[[(1+length(Focal_array))]] <- kin_out[["Focal"]]
-    daughter_array[[(1+length(daughter_array))]] <- kin_out[["d"]]
-    grand_daughter_array[[(1+length(grand_daughter_array))]] <- kin_out[["gd"]]
-    great_grand_daughter_array[[(1+length(great_grand_daughter_array))]] <- kin_out[["ggd"]]
-    mom_array[[(1+length(mom_array))]] <- kin_out[["m"]]
-    gran_array[[(1+length(gran_array))]] <- kin_out[["gm"]]
-    great_gran_array[[(1+length(great_gran_array))]] <- kin_out[["ggm"]]
-    younger_sis_array[[(1+length(younger_sis_array))]] <- kin_out[["ys"]]
-    older_sister_array[[(1+length(older_sister_array))]] <- kin_out[["os"]]
-    younger_aunt_array[[(1+length(younger_aunt_array))]] <- kin_out[["ya"]]
-    older_aunt_array[[(1+length(older_aunt_array))]] <- kin_out[["oa"]]
-    younger_niece_array[[(1+length(younger_niece_array))]] <- kin_out[["nys"]]
-    older_niece_array[[(1+length(older_niece_array))]] <- kin_out[["nos"]]
-    younger_cousin_array[[(1+length(younger_cousin_array))]] <- kin_out[["cya"]]
-    older_cousin_array[[(1+length(older_cousin_array))]] <- kin_out[["coa"]]
-    changing_pop_struct[[(1+length(changing_pop_struct))]] <- kin_out[["ps"]]
-    pb$tick()
-  }
-  tictoc::toc()
-  ## create a list of output kin -- each element a time-period specific list of matrices
-  ## label the kin names to match DemoKin:
-  relative_data <- list("Focal" = Focal_array,
-                        "d" = daughter_array,
-                        "gd" = grand_daughter_array,
-                        "ggd" = great_grand_daughter_array,
-                        "m" = mom_array,
-                        "gm" = gran_array,
-                        "ggm" = great_gran_array,
-                        "ys" = younger_sis_array,
-                        "os" = older_sister_array,
-                        "ya" = younger_aunt_array,
-                        "oa" = older_aunt_array,
-                        "nys" = younger_niece_array,
-                        "nos" = older_niece_array,
-                        "cya" = younger_cousin_array,
-                        "coa" = older_cousin_array)
-
-  relative_names <- names(relative_data)
-  ## create a nice data frame output
-  kin_full <- create_full_dists_df(relative_data,
-                                   relative_names,
-                                   output_years,
-                                   output_years[1],
-                                   na,
-                                   ns,
-                                   output_kin)
-  if(summary_kin){
-    kin_summary <- create_cumsum_df(relative_data,
-                                relative_names,
-                                output_years,
-                                output_years[1],
-                                na,
-                                ns,
-                                output_kin)
-    kin_out <- list(kin_full = kin_full, kin_summary = kin_summary)}
-  else{
-    kin_out <- kin_full
-  }
-  return(kin_out)
-}
-
-
-#' Title time invariant two-sex multi-state kin projections
-#'
-#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
-#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
-#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
-#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
-#' @param alpha scalar. birth ratio (male:female)
-#' @param na scalar. number of ages.
-#' @param ns scalar. number of stages.
-#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
-#' @param sex_Focal logical. Female or Male
-#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
-#'
-#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
-#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
-#' yielding the age*stage distribution of kin for each age of Focal
-
-all_kin_dy <- function(Uf,
-                       Um,
-                       Ff,
-                       Fm,
-                       alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
-                       na, ## na = number of ages
-                       ns, ## ns = number of stages
-                       Parity,
-                       sex_Focal, ## binary "F" or "M"
-                       Initial_stage_Focal){
-
-  n <- nrow(Uf) ## number of ages * stages for each sex
-
-  ## Projection matrices:
-
-  ## Uproj is a block diagonal matrix of block-structured Age*Stage matrices; independently over sex transfers individuals across stage and up age
-  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
-  ## Fproj is a Sex-block-structured matrix of block-structured Age*Stage matrices where males and females BOTH reproduce (by stage)
-  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  Fproj[1:n, 1:n] <- (1-alpha)*Ff ## Ff is Age*Stage block structured giving rate at which females in age-stage produce individuals in age-stage
-  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
-  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm ## Fm is Age*Stage block structured giving rate at which males in age-stage produce individuals in age-stage
-  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
-
-  ## Fprojstar is a Sex-block-structured matrix of block-structured Age*Stage matrices where ONLY females reproduce
-  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
-  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
-  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
-
-  ## The stable population structure is an age*stage*sex vector:
-  ##                                                            1:n gives the female age*stage structure
-  ##                                                            (1+n):2n gives the male age*stage structure
-  population_age_stage_structure <- SD(Uproj + Fprojstar)
-
-  ### Stable distribution of mothers needs adjusting if we work with parity
-  if(Parity){
-    Initial_stage_Focal <- 1
-
-    population_age_stage_of_parenting <- pi_mix_parity(Uf, Um, Ff, Fm, alpha, na, ns)
-
-    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
-
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-  else{
-    population_age_stage_of_parenting <- pi_mix(Uf, Um, Ff, Fm, alpha, na, ns)
-
-    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
-
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-
-  ####################################### The dynamics of Kinship, starting with Focal who is no longer a unit vector
-
-  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
-  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
-  m_t <- get_G(Um, na, ns)
-  G_tilde <- block_diag_function(list(f_t,m_t))
-  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  IC_Focal <- rep(0, 2*n)
-  if(sex_Focal == "Female"){
-    entry <- 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1}
-  else{
-    entry <- n + 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1
-  }
-
-  ### empty kin matrices for all of Focal's kin
-  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_sibs  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_niece_nephew  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_aunt_uncle  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_cousins  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-
-
-  ### Initial distributions for kin with non-zero deterministic initial conditions:
-  # Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
-  X_Focal[,1] <- IC_Focal
-  X_parents[, 1] <- parents_joint_age_stage
-
-  ### projection all kin with deterministic initial conditions
-  for(i in 1 : (na-1)){
-    X_Focal[,i+1] <- G_tilde %*% X_Focal[,i]
-    X_parents[, i+1] <- Uproj %*% X_parents[, i]
-    X_younger_sibs[,i+1] <- Uproj %*% X_younger_sibs[,i] + Fprojstar %*% X_parents[,i]
-    X_younger_niece_nephew[,i+1] <- Uproj %*% X_younger_niece_nephew[,i] + Fproj %*% X_younger_sibs[,i]
-    X_children[,i+1] <- Uproj %*% X_children[,i] + Fproj %*% X_Focal[,i]
-    X_grand_children[,i+1] <- Uproj %*% X_grand_children[,i] + Fproj %*% X_children[,i]
-    X_great_grand_children[,i+1] <- Uproj %*% X_great_grand_children[,i] + Fproj %*% X_grand_children[,i]
-  }
-
-  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
-  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
-  IC_f_grand_pars <- mothers_age_dist
-  IC_m_grand_pars <- fathers_age_dist
-  IC_f_great_grand_pars <- mothers_age_dist
-  IC_m_great_grand_pars <- fathers_age_dist
-  IC_older_sibs_f <- mothers_age_dist
-  IC_younger_aunts_uncles_f <- mothers_age_dist
-  IC_younger_aunts_uncles_m <- fathers_age_dist
-  IC_older_niece_nephew_f <- mothers_age_dist
-  for(ic in 1 : (na)){
-    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*X_parents[,ic] ## IC the sum of parents of Focal's parents,
-    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*X_grand_parents[,ic]
-    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*X_children[,ic]
-    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*X_grand_children[,ic]
-    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*X_younger_sibs[,ic]
-  }
-
-  ### Projections of grand parenst, older sibs, younger aunts/uncles, older nieces/nephews
-  for(i in 1: (na-1)){
-    X_grand_parents[, i+1] <- Uproj %*% X_grand_parents[, i]
-    X_great_grand_parents[, i+1] <- Uproj %*% X_great_grand_parents[, i]
-    X_older_sibs[,i+1] <- Uproj %*% X_older_sibs[,i]
-    X_older_niece_nephew[,i+1] <- Uproj %*% X_older_niece_nephew[,i] + Fproj %*% X_older_sibs[,i]
-    X_younger_aunts_uncles[,i+1] <- Uproj %*% X_younger_aunts_uncles[,i] + Fprojstar %*% X_grand_parents[,i]
-  }
-
-  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
-  ## older unts/uncles, older cousins, younger cousins
-  IC_older_aunt_uncle_f <- mothers_age_dist
-  IC_older_aunt_uncle_m <- fathers_age_dist
-  IC_older_cousins_f <- mothers_age_dist
-  IC_older_cousins_m <- fathers_age_dist
-  IC_younger_cousins_f <- mothers_age_dist
-  IC_younger_cousins_m <- fathers_age_dist
-  for(ic in 1 : (na-1)){
-    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*X_older_sibs[,ic]
-    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*X_older_niece_nephew[,ic]
-    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*X_younger_niece_nephew[,ic]
-  }
-
-  ## Projections of older unts/uncles, older cousins, younger cousins
-  for(i in 1: (na-1)){
-    X_older_aunt_uncle[,i+1] <- Uproj %*% X_older_aunt_uncle[,i]
-    X_older_cousins[,i+1] <- Uproj %*% X_older_cousins[,i] + Fproj %*% X_older_aunt_uncle[,i]
-    X_younger_cousins[,i+1] <- Uproj %*% X_younger_cousins[,i] + Fproj %*% X_younger_aunts_uncles[,i]
-  }
-
-  #### OUTPUT of all kin
-  return(list("Focal" = X_Focal,
-              "d" = X_children,
-              "gd" = X_grand_children,
-              "ggd" = X_great_grand_children,
-              "m" = X_parents,
-              "gm" = X_grand_parents,
-              "ggm" = X_great_grand_parents,
-              "os" = X_older_sibs,
-              "ys" = X_younger_sibs,
-              "nos" = X_older_niece_nephew,
-              "nys" = X_younger_niece_nephew,
-              "oa" = X_older_aunt_uncle,
-              "ya" = X_younger_aunts_uncles,
-              "coa" = X_older_cousins,
-              "cya" = X_younger_cousins,
-              "ps" = population_age_stage_structure
-  ))
-}
-
-
-#' Title time-variant two-sex multi-state kin projections
-#'
-#' @param Uf matrix (block structured). transfers female individuals across stages and advances their age (conditional on survial)
-#' @param Um matrix (block structured). transfers male individuals across stages and advances their age (conditional on survial)
-#' @param Ff matrix (block structured). accounts for female reproduction, and assigns newborns into given age*stage
-#' @param Fm matrix (block structured). accounts for male reproduction; assigns newborns into age-class, and stage
-#' @param alpha scalar. birth ratio (male:female)
-#' @param na scalar. number of ages.
-#' @param ns scalar. number of stages.
-#' @param Parity logical. If true then we omit mothers of parity 0, and re-scale the mother's age*stage of parenting
-#' @param sex_Focal logical. Female or Male
-#' @param Initial_stage_Focal numeric. Any natural number {1,2,3,4,...}
-#' @param previous_kin_Focal matrix. last years kinship output.
-#' @param prev_kin_children matrix. last years kinship output.
-#' @param prev_kin_grandchildren matrix. last years kinship output.
-#' @param prev_kin_greatgrandchildren matrix. last years kinship output.
-#' @param prev_kin_parents matrix. last years kinship output.
-#' @param prev_kin_grand_parents matrix. last years kinship output.
-#' @param prev_kin_older_sibs matrix. last years kinship output.
-#' @param prev_kin_younger_sibs matrix. last years kinship output.
-#' @param prev_kin_older_niece_nephew matrix. last years kinship output.
-#' @param prev_kin_younger_niece_nephew matrix. last years kinship output.
-#' @param prev_kin_older_aunts_uncles matrix. last years kinship output.
-#' @param prev_kin_younger_aunts_uncles matrix. last years kinship output.
-#' @param prev_kin_older_cousins matrix. last years kinship output.
-#' @param prev_kin_younger_cousins matrix. last years kinship output.
-#' @param previous_population_age_stage_structure vector. The transient "population structure" (age*stage distributed)
-#'
-#' @return a list of matrices. Each list entry represents a particular kin. Each kin is chacacterised by a matrix of dimension:
-#' nrow = 2* na * ns (2-sex age-stage structured) and ncol = na (Focal's age)
-#' yielding the age*stage distribution of kin for each age of Focal
-#'
-all_kin_dy_TV <- function(Uf,
-                          Um,
-                          Ff,
-                          Fm,
-                          alpha, ## alpha = sex ratio male:female (i.e., 1 - birth_female)
-                          na, ## number of ages
-                          ns, ## number of stages
-                          Parity,
-                          sex_Focal,
-                          Initial_stage_Focal,
-                          previous_kin_Focal,
-                          prev_kin_children,
-                          prev_kin_grandchildren,
-                          prev_kin_greatgrandchildren,
-                          prev_kin_parents,
-                          prev_kin_grand_parents,
-                          prev_kin_great_grand_parents,
-                          prev_kin_older_sibs,
-                          prev_kin_younger_sibs,
-                          prev_kin_older_niece_nephew,
-                          prev_kin_younger_niece_nephew,
-                          prev_kin_older_aunts_uncles,
-                          prev_kin_younger_aunts_uncles,
-                          prev_kin_older_cousins,
-                          prev_kin_younger_cousins,
-                          previous_population_age_stage_structure){
-
-  n <- nrow(Uf)
-  Uproj <- Matrix::Matrix(block_diag_function(list(Uf, Um)), sparse = TRUE)
-  Fproj <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  Fproj[1:n, 1:n] <- (1-alpha)*Ff
-  Fproj[(n+1):(2*n), 1:n] <- alpha*Ff
-  Fproj[1:n, (n+1):(2*n)] <- (1-alpha)*Fm
-  Fproj[(n+1):(2*n), (n+1):(2*n)] <- alpha*Fm
-  Fprojstar <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE) ## Block structured F_tilde
-  Fprojstar[1:n, 1:n] <- (1-alpha)*Ff
-  Fprojstar[(n+1):(2*n), 1:n] <- alpha*Ff
-
-  population_age_stage_structure <- previous_population_age_stage_structure
-  population_age_stage_structure <- population_age_stage_structure/sum(population_age_stage_structure)
-  population_age_stage_structure_next <- (Uproj + Fprojstar)%*%population_age_stage_structure
-
-  ### Stable distribution of mothers needs adjusting if we work with parity
-  if(Parity){
-    Initial_stage_Focal <- 1
-
-    population_age_stage_of_parenting <- pi_mix_TV_parity(Ff, Fm, alpha, na, ns, population_age_stage_structure)
-
-    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
-
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-  else{
-
-    population_age_stage_of_parenting <- pi_mix_TV(Ff, Fm, alpha, na, ns, population_age_stage_structure)
-
-    parents_joint_age_stage <- population_age_stage_of_parenting[[1]]
-
-    mothers_age_stage <- population_age_stage_of_parenting[[2]]
-    fathers_age_stage <- population_age_stage_of_parenting[[3]]
-
-    mothers_age_dist <- population_age_stage_of_parenting[[4]]
-    fathers_age_dist <- population_age_stage_of_parenting[[5]]
-
-  }
-
-  ### Focal requires its own dynamic: G_tilde constructed below tracks Focal's age*stage advancement over the time-scale
-  f_t <- get_G(Uf, na, ns) ## get_G function in "Functions_required.R"
-  m_t <- get_G(Um, na, ns)
-  G_tilde <- block_diag_function(list(f_t,m_t))
-  X_Focal  <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  IC_Focal <- rep(0, 2*n)
-  if(sex_Focal == "Female"){
-    entry <- 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1}
-  else{
-    entry <- n + 1 + (Initial_stage_Focal-1)*na
-    IC_Focal[entry] <- 1
-  }
-
-  ### empty kin matrices for all of Focal's kin
-  X_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_children <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_great_grand_parents <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_sibs <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_niece_nephew <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_aunt_uncle <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_aunts_uncles <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_older_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-  X_younger_cousins <- Matrix::Matrix(nrow = (2*n), ncol = na, data = 0, sparse = TRUE)
-
-  ### Initial distributions for kin with non-zero deterministic initial conditions:
-  ## Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews
-  X_Focal[,1] <- IC_Focal
-  X_parents[, 1] <- parents_joint_age_stage
-  ### projection all above kin with deterministic initial conditions
-  for(i in 1 : (na-1)){
-    X_Focal[,i+1] <- G_tilde %*% previous_kin_Focal[,i]
-    X_parents[, i+1] <- Uproj %*% prev_kin_parents[, i]
-    X_younger_sibs[,i+1] <- Uproj %*% prev_kin_younger_sibs[,i] + Fprojstar %*% prev_kin_parents[,i]
-    X_younger_niece_nephew[,i+1] <- Uproj %*% prev_kin_younger_niece_nephew[,i] + Fproj %*% prev_kin_younger_sibs[,i]
-    X_children[,i+1] <- Uproj %*% prev_kin_children[,i] + Fproj %*% previous_kin_Focal[,i]
-    X_grand_children[,i+1] <- Uproj %*% prev_kin_grandchildren[,i] + Fproj %*% prev_kin_children[,i]
-    X_great_grand_children[,i+1] <- Uproj %*% prev_kin_greatgrandchildren[,i] + Fproj %*% prev_kin_grandchildren[,i]
-  }
-
-  ### IC for kin which are derived from above kin (Focal, parents, children, grand+great children, younger siblings, and younger nieces/nehpews):
-  # grand parents, older sibs, younger aunts/uncles, older nieces/nephews
-  IC_f_grand_pars <- mothers_age_dist
-  IC_m_grand_pars <- fathers_age_dist
-  IC_f_great_grand_pars <- mothers_age_dist
-  IC_m_great_grand_pars <- fathers_age_dist
-  IC_younger_aunts_uncles_f <- mothers_age_dist
-  IC_younger_aunts_uncles_m <- fathers_age_dist
-  IC_older_sibs_f <- mothers_age_dist
-  IC_older_niece_nephew_f <- mothers_age_dist
-  for(ic in 1 : (na)){
-    X_grand_parents[, 1] <- X_grand_parents[, 1] + (IC_f_grand_pars[ic] + IC_m_grand_pars[ic])*prev_kin_parents[,ic] ## IC the sum of parents of Focal's parents,
-    X_great_grand_parents[, 1] <- X_great_grand_parents[, 1] + (IC_f_great_grand_pars[ic] + IC_m_great_grand_pars[ic])*prev_kin_grand_parents[,ic]
-    X_older_sibs[,1] <- X_older_sibs[,1] + IC_older_sibs_f[ic]*prev_kin_children[,ic]
-    X_older_niece_nephew[,1] <- X_older_niece_nephew[,1] + IC_older_niece_nephew_f[ic]*prev_kin_grandchildren[,ic]
-    X_younger_aunts_uncles[,1] <- X_younger_aunts_uncles[,1] + (IC_younger_aunts_uncles_f[ic] + IC_younger_aunts_uncles_m[ic])*prev_kin_younger_sibs[,ic]
-  }
-
-  ### Projections of older sibs, younger aunts/uncles, older nieces/nephews
-  for(i in 1: (na-1)){
-    X_grand_parents[, i+1] <- Uproj %*% prev_kin_grand_parents[, i]
-    X_great_grand_parents[, i+1] <- Uproj %*% prev_kin_great_grand_parents[, i]
-    X_older_sibs[,i+1] <- Uproj %*% prev_kin_older_sibs[,i]
-    X_older_niece_nephew[,i+1] <- Uproj %*% prev_kin_older_niece_nephew[,i] + Fproj %*% prev_kin_older_sibs[,i]
-    X_younger_aunts_uncles[,i+1] <- Uproj %*% prev_kin_younger_aunts_uncles[,i] + Fprojstar %*% prev_kin_grand_parents[,i]
-  }
-
-  ### IC for kin which are derived from above kin (older sibs, younger aunts/uncles, older nieces/nephews):
-  ## older unts/uncles, older cousins, younger cousins
-  IC_older_aunt_uncle_f <- mothers_age_dist
-  IC_older_aunt_uncle_m <- fathers_age_dist
-  IC_older_cousins_f <- mothers_age_dist
-  IC_older_cousins_m <- fathers_age_dist
-  IC_younger_cousins_f <- mothers_age_dist
-  IC_younger_cousins_m <- fathers_age_dist
-  for(ic in 1 : (na-1)){
-    X_older_aunt_uncle[,1] <- X_older_aunt_uncle[,1] + (IC_older_aunt_uncle_f[ic] + IC_older_aunt_uncle_m[ic])*prev_kin_older_sibs[,ic]
-    X_older_cousins[,1] <- X_older_cousins[,1] + (IC_older_cousins_f[ic] + IC_older_cousins_m[ic])*prev_kin_older_niece_nephew[,ic]
-    X_younger_cousins[,1] <- X_younger_cousins[,1] + (IC_younger_cousins_f[ic] + IC_younger_cousins_m[ic])*prev_kin_younger_niece_nephew[,ic]
-  }
-
-  ## Projections of older unts/uncles, older cousins, younger cousins
-  for(i in 1: (na-1)){
-    X_older_aunt_uncle[,i+1] <- Uproj %*% prev_kin_older_aunts_uncles[,i]
-    X_older_cousins[,i+1] <- Uproj %*% prev_kin_older_cousins[,i] + Fproj %*% prev_kin_older_aunts_uncles[,i]
-    X_younger_cousins[,i+1] <- Uproj %*% prev_kin_younger_cousins[,i] + Fproj %*% prev_kin_younger_aunts_uncles[,i]
-  }
-
-  return(list("Focal" = X_Focal,
-              "d" = X_children,
-              "gd" = X_grand_children,
-              "ggd" = X_great_grand_children,
-              "m" = X_parents,
-              "gm" = X_grand_parents,
-              "ggm" = X_great_grand_parents,
-              "os" = X_older_sibs,
-              "ys" = X_younger_sibs,
-              "nos" = X_older_niece_nephew,
-              "nys" = X_younger_niece_nephew,
-              "oa" = X_older_aunt_uncle,
-              "ya" = X_younger_aunts_uncles,
-              "coa" = X_older_cousins,
-              "cya" = X_younger_cousins,
-              "ps" = population_age_stage_structure_next))
-}
-
-################## Create data frame output
-
-## Use of "pipe" (don't understand the name, but hey)
-`%>%` <- magrittr::`%>%`
-
-#' Title Accumulated kin by each age of Focal, for each time period, and cohort of birth
-#'
-#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
-#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
-#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
-#' @param years vector. The timescale on which we implement the kinship model.
-#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
-#' @param na numeric. Number of ages.
-#' @param ns numeric. Number of stages.
-#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
-#'
-#' @return A data frame which gives for each age of Focal at each year in the timescale, Focal's experienced number kin demarcated by stages (summed over all ages)
-#'
-create_cumsum_df <- function(kin_matrix_lists,
-                             kin_names,
-                             years,
-                             start_year,
-                             na,
-                             ns,
-                             specific_kin = NULL){
-  df_year_list <- list()
-  for(j in years){
-    ii <- as.numeric(j) - start_year + 1
-    df_list <- list()
-    for(i in 1 : length(kin_names)){
-      kin_member <- kin_names[[i]]
-      kin_data <- kin_matrix_lists[[i]]
-      kin_data <- kin_data[[ii]]
-      df <- as.data.frame(as.matrix(kin_data))
-      dims <- dim( kin_data)
-      nr <- dims[1]
-      nc <- dims[2]
-      female_kin <- df[1:(nr/2), 1:nc]
-      male_kin <- df[ (1+nr/2) : nr, 1:nc]
-      female_kin$stage <- rep(seq(1, ns), na)
-      male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1)), each = ns)
-      male_kin$age <- rep(seq(0, (na-1)), each = ns)
-      female_kin$Sex <- "Female"
-      male_kin$Sex <- "Male"
-      both_kin <- rbind(female_kin, male_kin)
-      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
-        dplyr::group_by(variable, stage, Sex) %>%
-        dplyr::summarise(num = sum(value)) %>%
-        dplyr::ungroup()
-      both_kin <- both_kin %>% dplyr::transmute(age_focal = variable,
-                                                stage_kin = as.factor(stage),
-                                                count = num,
-                                                sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal)) - 1
-      df <- both_kin
-      df$year <- j
-      df$group <- kin_member
-      df_list[[length(df_list)+1]] <- df
-    }
-    df_list <- do.call("rbind", df_list)
-    df_year_list[[(1+length(df_year_list))]] <- df_list
-  }
-  df_year_list <- do.call("rbind", df_year_list)
-  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
-                                                 cohort_factor = as.factor(cohort))
-  if(!is.null(specific_kin)){
-    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
-  }
-  return(df_year_list)
-}
-
-#' Title joint age*stage distributions of kin by each age of Focal, for each time period, and cohort of birth
-#'
-#' @param kin_matrix_lists list of lists of kin matrices: list( list(X_focal), list(X_parents), ... ). Outer list is length 14  = number of kin. Inner lists have lenght = timescale
-#'                 so list(X_focal) = list(X_focal[year1],X_focal[year2],...,X_focal[yearlast])
-#' @param kin_names list of characters. Corresponding to above lists: list("F","m",....)
-#' @param years vector. The timescale on which we implement the kinship model.
-#' @param start_year . First year of varying vital rates (e.g., if years = 1990:2000 then start_year = 1990)
-#' @param na numeric. Number of ages.
-#' @param ns numeric. Number of stages.
-#' @param specific_kin character. names of kin we wish to analyse, e.g., list("os","ys"). If null returns all 14.
-#'
-#' @return A data frame which gives for each age of Focal at each year in the timescale, the full age*stage dist of kin
-#'
-create_full_dists_df <- function(kin_matrix_lists,
-                                 kin_names,
-                                 years,
-                                 start_year,
-                                 na,
-                                 ns,
-                                 specific_kin = NULL){
-  df_year_list <- list()
-  for(j in years){
-    ii <- as.numeric(j) - start_year + 1
-    df_list <- list()
-    for(i in 1 : length(kin_names)){
-      kin_member <- kin_names[[i]]
-      kin_data <- kin_matrix_lists[[i]]
-      kin_data <- kin_data[[ii]]
-      df <- as.data.frame(as.matrix(kin_data))
-      dims <- dim( kin_data)
-      nr <- dims[1]
-      nc <- dims[2]
-      female_kin <- df[1:(nr/2), 1:nc]
-      male_kin <- df[ (1+nr/2) : nr, 1:nc]
-      female_kin$stage <- rep(seq(1, ns), na)
-      male_kin$stage <- rep(seq(1, ns), na)
-      female_kin$age <- rep(seq(0, (na-1)), each = ns)
-      male_kin$age <- rep(seq(0, (na-1)), each = ns)
-      female_kin$Sex <- "Female"
-      male_kin$Sex <- "Male"
-      both_kin <- rbind(female_kin, male_kin)
-      both_kin <- both_kin %>% reshape2::melt(id = c("age","stage","Sex")) %>%
-        dplyr::transmute(age_focal = variable,
-                         age_kin = age,
-                         stage_kin = as.factor(stage),
-                         count = value,
-                         sex_kin = Sex)
-      both_kin$age_focal <- as.numeric(gsub("[^0-9.-]", "", both_kin$age_focal))-1
-      df <- both_kin
-      df$year <- j
-      df$group <- kin_member
-      df_list[[length(df_list)+1]] <- df
-    }
-    df_list <- do.call("rbind", df_list)
-    df_year_list[[(1+length(df_year_list))]] <- df_list
-  }
-  df_year_list <- do.call("rbind", df_year_list)
-  df_year_list <- df_year_list %>% dplyr::mutate(cohort = as.numeric(year) - as.numeric(age_focal),
-                                                 cohort_factor = as.factor(cohort))
-  if(!is.null(specific_kin)){
-    df_year_list <- df_year_list %>% dplyr::filter(group %in% specific_kin)
-  }
-  return(df_year_list)
-}
-
-
-
-## Construct a matrix composed as a direct sum of a list of matrices
-block_diag_function <- function(mat_list){
-  s = length(mat_list)
-  u1 = mat_list[[1]]
-  dims <- dim(u1)
-  r = dims[1]
-  diagmat <- Matrix::Matrix(nrow = (r*s), ncol = (r*s), data = 0, sparse = TRUE)
-  for(i in 1:s){
-    diagmat = diagmat + kronecker(E_matrix(i,i,s,s), mat_list[[i]])
-  }
-  return(diagmat)
-}
-
-## Construct a matrix which transfers Focal across stages, while ensuring Focal survives with probability 1
-get_G <- function(U, na, ns){
-  sig <- Matrix::t(rep(1,na*ns)) %*% U
-  diag <- Matrix::diag(sig[1,])
-  G <- U %*% MASS::ginv(diag)
-  return(G)
-}
-
-#' Mixing distributions for the time-invariant multi-state 2-sex model: Non-parity case
-#'
-#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
-#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix <- function(Uf, Um, Ff, Fm, alpha, na, ns){
-  n <- length(Uf[1,])
-  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  F_block[1:n, 1:n] <- (1-alpha)*Ff
-  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
-  A <- block_diag_function(list(Uf,Um)) + F_block
-  stable_dist_vec <- SD(A)
-  ### Joint distributions
-  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  ### Age distributions
-  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
-  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
-  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
-}
-
-#' Mixing distributions for the time-variant multi-state 2-sex model: Non-parity case
-#'
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix_TV <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
-  n <- length(Ff[1,])
-  ### Joint distributions
-  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  ### Age distributions
-  pi_F <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_f)
-  pi_M <- kronecker( diag(na), Matrix::t(rep(1, ns)) ) %*% (pi_m)
-  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
-}
-
-#' Mixing distributions for the time-invariant multi-state 2-sex model: Parity-specific case
-#'
-#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
-#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix_parity <- function(Uf, Um, Ff, Fm, alpha, na, ns){
-  n <- length(Uf[1,])
-  F_block <- Matrix::Matrix(nrow = (2*n), ncol = (2*n), data = 0, sparse = TRUE)
-  F_block[1:n, 1:n] <- (1-alpha)*Ff
-  F_block[ (1+n):(2*n), 1:n] <- alpha*Ff
-  A <- block_diag_function(list(Uf,Um)) + F_block
-  stable_dist_vec <- SD(A)
-  pi_f <-  Matrix::t( rep(1, na*ns) %*% Ff )*stable_dist_vec[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1, na*ns) %*% Fm )*stable_dist_vec[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  m_mat <- pi_f %*% Matrix::t(rep(1,na))
-  d_mat <- pi_m %*% Matrix::t(rep(1,na))
-  pi_F <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
-  pi_M <- kronecker( diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
-  for(i in 1:na){
-    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
-    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
-  }
-  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
-  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
-  ### Joint distributions
-  pi_f <- out_mum %*% pi_F
-  pi_m <- out_dad %*% pi_M
-  return(list(rbind(pi_f,pi_m), pi_f, pi_m, pi_F, pi_M))
-}
-
-#' Mixing distributions for the time-variant multi-state 2-sex model: Parity-specific case
-#'
-#' @param Uf matrix. Block-structured matrix which transfers females over stage and advances their age
-#' @param Um matrix. Block-structured matrix which transfers males over stage and advances their age
-#' @param Ff matrix. Block-structured matrix which counts reproduction by females and assigns newborns an age and stage
-#' @param Fm matrix. Block-structured matrix which counts reproduction by males and assigns newborns an age and stage
-#' @param alpha scalar. Birth ratio male:female
-#' @param na scalar. Number of age-classes
-#' @param ns scalar. Number of stages
-#' @param previous_age_stage_dist vector. Last years population structure (age*stage*sex full distribution)
-#'
-#' @return list (of vectors). list[[1]] = full age*stage*sex distribution
-#'                            list[[2]] = female age*stage distribution normalised
-#'                            list[[3]] = male age*stage distribution normalised
-#'                            list[[4]] = female marginal age distribution normalised
-#'                            list[[5]] = male marginal age distribution normalised
-#'
-pi_mix_TV_parity <- function(Ff, Fm, alpha, na, ns, previous_age_stage_dist){
-  n <- length(Ff[1,])
-  pi_f <-  Matrix::t( rep(1,na*ns) %*% Ff )*previous_age_stage_dist[1:n]
-  pi_f <- pi_f / abs(sum(pi_f))
-  pi_m <-  Matrix::t( rep(1,na*ns) %*% Fm )*previous_age_stage_dist[(1+n):(2*n)]
-  pi_m <- pi_m / abs(sum(pi_m))
-  m_mat <- pi_f %*% Matrix::t(rep(1,na))
-  d_mat <- pi_m %*% Matrix::t(rep(1,na))
-  pi_F  <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_f
-  pi_M <- kronecker( Matrix::diag(1, na), Matrix::t(rep(1,ns)) ) %*% pi_m
-  for(i in 1:na){
-    m_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% m_mat[,i]
-    d_mat[,i] <- kronecker( E_matrix(i,i,na,na) , Matrix::diag( c(0, rep(1, ns-1)) ) ) %*% d_mat[,i]
-  }
-  out_mum <- m_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(m_mat)))
-  out_dad <- d_mat %*% MASS::ginv(Matrix::diag(Matrix::colSums(d_mat)))
-  ### Joint distributions
-  pi_f <- out_mum %*% pi_F
-  pi_m <- out_dad %*% pi_M
-  return(list(rbind(pi_f,pi_m),pi_f,pi_m,pi_F,pi_M))
-}
-
-
-######################################################### Some useful utility functions required
-
-
-###################################################### Eigen-decomposition of a matrix
-
-# Calculate the spectral radius of a matrix (growth rate in Demographics)
-lambda <- function(PM) {
-  lead_eig <- (abs(eigen(PM, only.values = TRUE)$values))
-  lead_eig <- lead_eig[which.max(lead_eig)]
-  return(lead_eig)
-}
-# Find the column-eigenvector corresponding to the spectral radius (Stable population structure in Demographics)
-SD <- function(PM) {
-  spectral_stuff <- eigen(PM)
-  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
-  # normalise...
-  vec_lambda <- spectral_stuff/sum(spectral_stuff)
-  return(vec_lambda)
-}
-# Find the row-eigenvector corresponding to the spectral radius (Stable reproductive values in Demographics)
-RD <- function(PM) {
-  spectral_stuff <- eigen(t(PM))
-  spectral_stuff <- Re(spectral_stuff$vectors[, which.max(abs(spectral_stuff$values))])
-  # normalise...
-  vec_lambda <- spectral_stuff/sum(spectral_stuff)
-  return(vec_lambda)
-}
-
-###################################################### Useful matrix operations
-
-## Constructing a unit vector with a 1 in the ith position
-e_vector <- function(i, n){
-  e <- rep(0, n)
-  e[i] <- 1
-  return(e)
-}
-## Creating a matrix of zeros with a 1 in the i,j-th entry
-E_matrix <- function(i,j,n,m){
-  E <- Matrix::Matrix(nrow = (n), ncol = (m), data = 0, sparse = TRUE)
-  E[i,j] <- 1
-  return(E)
-
-}
-## Creating the Vec-commutation matrix
-K_perm_mat <- function(n,m){
-  perm <- Matrix::Matrix(nrow = (n*m), ncol = (n*m), data = 0, sparse = TRUE)
-  for(i in 1:n){
-    for(j in 1:m){
-      perm = perm + kronecker( E_matrix(i,j,n,m) , Matrix::t(E_matrix(i,j,n,m)) )
-    }
-  }
-  return(perm)
-}
-
-
-
-
-
-
-
-

From e28ef282eae2442225f2a448fa516e4c0f5962f7 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 5 Jun 2025 20:42:00 -0300
Subject: [PATCH 88/89] show plot readme

---
 README.Rmd                               |    2 +-
 README.md                                |    2 +
 man/figures/README-unnamed-chunk-3-1.svg | 2844 ++++++++++++++++++++++
 3 files changed, 2847 insertions(+), 1 deletion(-)
 create mode 100644 man/figures/README-unnamed-chunk-3-1.svg

diff --git a/README.Rmd b/README.Rmd
index 50c6f06..871eaef 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -53,7 +53,7 @@ library(DemoKin)
 
 Here's a basic example of how to use DemoKin:
 
-```{r, eval = F}
+```{r, eval = T, warning=FALSE, message=F, dev="svg"}
 
 # Run a one-sex time-invariant kinship model using Swedish data from 2015
 kin_results <- kin(
diff --git a/README.md b/README.md
index 51c4aab..0d69d9d 100644
--- a/README.md
+++ b/README.md
@@ -74,6 +74,8 @@ kin_results$kin_summary %>%
   )
 ```
 
+<img src="man/figures/README-unnamed-chunk-3-1.svg" width="100%" />
+
 ## Documentation
 
 For detailed documentation, please visit the [DemoKin
diff --git a/man/figures/README-unnamed-chunk-3-1.svg b/man/figures/README-unnamed-chunk-3-1.svg
new file mode 100644
index 0000000..74ba745
--- /dev/null
+++ b/man/figures/README-unnamed-chunk-3-1.svg
@@ -0,0 +1,2844 @@
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From 9267590e89cf8c053b3c81c7d997e354e7345b85 Mon Sep 17 00:00:00 2001
From: Ivan Williams <ivanwilliams1985@gmail.com>
Date: Thu, 5 Jun 2025 20:57:42 -0300
Subject: [PATCH 89/89] improve plot readme

---
 README.Rmd                               |    2 +-
 README.md                                |    2 +-
 man/figures/README-unnamed-chunk-3-1.svg | 2420 +++++++++++-----------
 3 files changed, 1217 insertions(+), 1207 deletions(-)

diff --git a/README.Rmd b/README.Rmd
index 871eaef..f0f0483 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -53,7 +53,7 @@ library(DemoKin)
 
 Here's a basic example of how to use DemoKin:
 
-```{r, eval = T, warning=FALSE, message=F, dev="svg"}
+```{r, eval = T, warning=FALSE, message=F, dev="svg", fig.width=6, fig.height=4, out.width="70%"}
 
 # Run a one-sex time-invariant kinship model using Swedish data from 2015
 kin_results <- kin(
diff --git a/README.md b/README.md
index 0d69d9d..b88b6eb 100644
--- a/README.md
+++ b/README.md
@@ -74,7 +74,7 @@ kin_results$kin_summary %>%
   )
 ```
 
-<img src="man/figures/README-unnamed-chunk-3-1.svg" width="100%" />
+<img src="man/figures/README-unnamed-chunk-3-1.svg" width="70%" />
 
 ## Documentation
 
diff --git a/man/figures/README-unnamed-chunk-3-1.svg b/man/figures/README-unnamed-chunk-3-1.svg
index 74ba745..58a0d6c 100644
--- a/man/figures/README-unnamed-chunk-3-1.svg
+++ b/man/figures/README-unnamed-chunk-3-1.svg
@@ -1,5 +1,5 @@
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