Scalable Joint Species Distribution Modeling

A scalable method to estimate joint Species Distribution Models (jSDMs) for big community datasets based on a Monte Carlo approximation of the joint likelihood. The numerical approximation is based on 'PyTorch' and 'reticulate', and can be run on CPUs and GPUs alike. The method is described in Pichler & Hartig (2021) . The package contains various extensions, including support for different response families, ability to account for spatial autocorrelation, and deep neural networks instead of the linear predictor in jSDMs.


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install.packages("sjSDM")

1.0.0 by Maximilian Pichler, 18 days ago


https://theoreticalecology.github.io/s-jSDM/


Report a bug at https://github.com/TheoreticalEcology/s-jSDM/issues


Browse source code at https://github.com/cran/sjSDM


Authors: Maximilian Pichler [aut, cre] , Florian Hartig [aut] , Wang Cai [ctb]


Documentation:   PDF Manual  


GPL-3 license


Imports reticulate, stats, mvtnorm, utils, rstudioapi, abind, graphics, grDevices, Metrics, parallel, mgcv, Ternary, cli, crayon, ggplot2, checkmate, mathjaxr

Suggests testthat, knitr, rmarkdown


See at CRAN