Package: jSDM 0.2.7

Ghislain Vieilledent

jSDM: Joint Species Distribution Models

Fits joint species distribution models ('jSDM') in a hierarchical Bayesian framework (Warton and al. 2015 <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation efficiency.

Authors:Ghislain Vieilledent [aut, cre], Jeanne Clément [aut], Frédéric Gosselin [ctb], CIRAD [cph, fnd]

jSDM_0.2.7.tar.gz
jSDM_0.2.7.zip(r-4.7-x86_64)jSDM_0.2.7.zip(r-4.7-arm64)jSDM_0.2.7.zip(r-4.6-x86_64)jSDM_0.2.7.zip(r-4.6-arm64)
jSDM_0.2.7.tar.gz(r-4.7-arm64)jSDM_0.2.7.tar.gz(r-4.7-x86_64)jSDM_0.2.7.tar.gz(r-4.6-arm64)jSDM_0.2.7.tar.gz(r-4.6-x86_64)
jSDM_0.2.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
jSDM/json (API)

# Install 'jSDM' in R:
install.packages('jSDM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ghislainv/jsdm/issues

Pkgdown/docs site:https://ecology.ghislainv.fr

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

gslopenblascpp

3.89 score 77 scripts 369 downloads 12 exports 12 dependencies

Last updated from:970aa32ce3. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK497
linux-devel-x86_64OK484
source / vignettesOK661
linux-release-arm64OK482
linux-release-x86_64OK523
windows-devel-arm64OK732
windows-devel-x86_64OK578
windows-release-arm64OK746
windows-release-x86_64OK578
wasm-releaseOK253

Exports:get_enviro_corget_residual_corinv_logitjSDM_binomial_logitjSDM_binomial_probitjSDM_binomial_probit_long_formatjSDM_binomial_probit_sp_constrainedjSDM_gaussianjSDM_poisson_loglogitplot_associationsplot_residual_cor

Dependencies:codacodetoolscorrplotdoParallelforeachiteratorslatticeMASSRcppRcppArmadilloRcppGSLstringi

Bayesian inference methods
Bernoulli distribution with probit link function | Model definition | Conjugate priors | Fixed species effects | Random site effects | Random site effect variance | Gibbs sampler principle | Gibbs sampler using conjuate priors | Binomial distribution with logit link function | Priors used | Adaptive Metropolis algorithm principle | Gibbs sampler using adaptative Metropolis algorithm | Poisson distribution with log link function | References
Rendered fromproof.Rmdusingknitr::rmarkdown

Last update: 2025-11-03
Started: 2022-03-08

Get started with jSDM
jSDM package | Load librairies | Bernoulli probit regression | Definition of the model | Occurrence data-set | Parameter inference | Analysis of the results | Matrice of correlations | Predictions | References
Rendered fromjSDM.Rmdusingknitr::rmarkdown

Last update: 2025-11-03
Started: 2019-07-02