added crosslink to vtreat article

Author: NinaZumel

PartialPooling_R/PartialPooling_R.Rmd

@@ -15,7 +15,7 @@ Banaue rice terraces. Photo: <a href="https://www.flickr.com/photos/london/31934
 <br><br>
 
 
-In a previous article, we showed the use of partial pooling, or hierarchical/multilevel models, for level coding high-cardinality categorical variables in [`vtreat`](https://winvector.github.io/vtreat/). In this article, we will discuss a little more about the how and why of partial pooling in [`R`](https://www.r-project.org).
+In a [previous article](https://github.com/WinVector/vtreat/blob/main/Examples/CustomLevelCoding/CustomLevelCoding.md), we showed the use of partial pooling, or hierarchical/multilevel models, for level coding high-cardinality categorical variables in [`vtreat`](https://winvector.github.io/vtreat/). In this article, we will discuss a little more about the how and why of partial pooling in [`R`](https://www.r-project.org).
 
 We will use the `lme4` package to fit the hierarchical models. The acronym "lme" stands for "linear mixed-effects" models: models that combine so-called "fixed effects" and "random effects" in a single (generalized) linear model. The `lme4` documentation uses the random/fixed effects terminology, but we are going to follow Gelman and Hill, and avoid the use of the terms "fixed" and "random" effects.
 

PartialPooling_R/PartialPooling_R.html

@@ -589,7 +589,8 @@ <h4 class="author">Nina Zumel</h4>
 </figcaption>
 </figure>
 <p><br><br></p>
-<p>In a previous article, we showed the use of partial pooling, or
+<p>In a <a href="https://github.com/WinVector/vtreat/blob/main/Examples/CustomLevelCoding/CustomLevelCoding.md">previous
+article</a>, we showed the use of partial pooling, or
 hierarchical/multilevel models, for level coding high-cardinality
 categorical variables in <a href="https://winvector.github.io/vtreat/"><code>vtreat</code></a>. In
 this article, we will discuss a little more about the how and why of

PartialPooling_R/PartialPooling_R.md

@@ -15,7 +15,7 @@ Banaue rice terraces. Photo: <a href="https://www.flickr.com/photos/london/31934
 <br><br>
 
 
-In a previous article, we showed the use of partial pooling, or hierarchical/multilevel models, for level coding high-cardinality categorical variables in [`vtreat`](https://winvector.github.io/vtreat/). In this article, we will discuss a little more about the how and why of partial pooling in [`R`](https://www.r-project.org).
+In a [previous article](https://github.com/WinVector/vtreat/blob/main/Examples/CustomLevelCoding/CustomLevelCoding.md), we showed the use of partial pooling, or hierarchical/multilevel models, for level coding high-cardinality categorical variables in [`vtreat`](https://winvector.github.io/vtreat/). In this article, we will discuss a little more about the how and why of partial pooling in [`R`](https://www.r-project.org).
 
 We will use the `lme4` package to fit the hierarchical models. The acronym "lme" stands for "linear mixed-effects" models: models that combine so-called "fixed effects" and "random effects" in a single (generalized) linear model. The `lme4` documentation uses the random/fixed effects terminology, but we are going to follow Gelman and Hill, and avoid the use of the terms "fixed" and "random" effects.