sjSDM
Scalable joint species distribution modelling, estimating the full species covariance matrix rather than approximating it.
An R package for fast and accurate joint species distribution models (jSDMs) — multivariate generalized linear mixed models that describe how a whole community of species responds to environmental predictors, to space, and to each other.
The difficult part of a jSDM is the species–species covariance. Most implementations approximate it with latent variables to keep the computation tractable. sjSDM instead estimates the full covariance matrix, using numerical simulation and a PyTorch backend so that it stays fast enough for large community datasets. Alongside the model fitting it provides the tools needed to interpret the result: ANOVA and variation partitioning, metacommunity-structure analysis, and plotting.
Developed by Maximilian Pichler, with Florian Hartig. The method is described in Pichler & Hartig (2021) in Methods in Ecology and Evolution.