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It may be possible to get a speed up from deterministic projections in big landscapes by using sparse matrix classes
For a 3-stage, 2000 patch dispersal model, switching from matrix to Matrix (and adding import & dealing with classes) here doesn't slow down pop:::as.matrix.dynamic() much (7.3s vs 3.9s) and results in much faster multiplications (1.2s vs 5.3s).
This only skips true 0s, so is as accurate as the dense matrix version. If the matrix construction end can be sped up, this may be worthwhile in all dispersal cases.
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