Package: sjSDM
Type: Package
Title: Scalable Joint Species Distribution Modeling
Version: 1.0.7
Authors@R: 
    c(person(given = "Maximilian",
             family = "Pichler",
             role = c("aut", "cre"),
             email = "maximilian.pichler@biologie.uni-regensburg.de",
             comment = c(ORCID = "0000-0003-2252-8327")),
      person(given = "Florian",
             family = "Hartig",
             role = "aut",
             email = "florian.hartig@biologie.uni-regensburg.de",
             comment = c(ORCID = "0000-0002-6255-9059")),
      person(given = "Wang",
             family = "Cai",
             role = "ctb",
             email = "caiwang0503@163.com"))
Description: A scalable and fast method for estimating joint Species
        Distribution Models (jSDMs) for big community data, including
        eDNA data. The package estimates a full (i.e. non-latent) jSDM
        with different response distributions (including the
        traditional multivariate probit model). The package allows to
        perform variation partitioning (VP) / ANOVA on the fitted
        models to separate the contribution of environmental, spatial,
        and biotic associations. In addition, the total R-squared can
        be further partitioned per species and site to reveal the
        internal metacommunity structure, see Leibold et al.,
        <doi:10.1111/oik.08618>. The internal structure can then be
        regressed against environmental and spatial distinctiveness,
        richness, and traits to analyze metacommunity assembly
        processes.  The package includes support for accounting for
        spatial autocorrelation and the option to fit responses using
        deep neural networks instead of a standard linear predictor. As
        described in Pichler & Hartig (2021)
        <doi:10.1111/2041-210X.13687>, scalability is achieved by using
        a Monte Carlo approximation of the joint likelihood implemented
        via 'PyTorch' and 'reticulate', which can be run on CPUs or
        GPUs.
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 4.1.0)
Imports: reticulate, stats, mvtnorm, utils, rstudioapi, abind,
        graphics, grDevices, Metrics, parallel, mgcv, cli, crayon,
        ggplot2, checkmate, mathjaxr, beeswarm, qgam, scales, viridis
Suggests: testthat, knitr, rmarkdown, iml, fields
RoxygenNote: 7.3.3
URL: https://github.com/TheoreticalEcology/s-jSDM/
BugReports: https://github.com/TheoreticalEcology/s-jSDM/issues
Roxygen: list(old_usage = FALSE, markdown = TRUE)
VignetteBuilder: knitr
RdMacros: mathjaxr
Config/pak/sysreqs: cmake make libpng-dev libuv1-dev python3 zlib1g-dev
Repository: https://theoreticalecology.r-universe.dev
Date/Publication: 2026-06-11 11:49:27 UTC
RemoteUrl: https://github.com/theoreticalecology/s-jsdm
RemoteRef: HEAD
RemoteSha: 855e785863e96a44940776410c76dbdc6d4b0ab3
RemoteSubdir: sjSDM
NeedsCompilation: no
Packaged: 2026-06-11 15:30:12 UTC; root
Author: Maximilian Pichler [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-2252-8327>),
  Florian Hartig [aut] (ORCID: <https://orcid.org/0000-0002-6255-9059>),
  Wang Cai [ctb]
Maintainer: Maximilian Pichler <maximilian.pichler@biologie.uni-regensburg.de>