Package: multAbund 0.03.9002

multAbund: Model Multivariate Abundance and Occurence Using the Dirichlet Process Prior

This package provides RJMCMC samplers to make Bayesian inference for Joint Species Distribution models (JSDM) using a Dirichlet Process clustering method. The DP clustering method allows species to be grouped into functional guilds for modeling between species associations in abundance or occurence values at surveyed sites.

Authors:Devin S. Johnson

multAbund_0.03.9002.tar.gz
multAbund_0.03.9002.zip(r-4.7)multAbund_0.03.9002.zip(r-4.6)multAbund_0.03.9002.zip(r-4.5)
multAbund_0.03.9002.tgz(r-4.6-x86_64)multAbund_0.03.9002.tgz(r-4.6-arm64)multAbund_0.03.9002.tgz(r-4.5-x86_64)multAbund_0.03.9002.tgz(r-4.5-arm64)
multAbund_0.03.9002.tar.gz(r-4.7-arm64)multAbund_0.03.9002.tar.gz(r-4.6-arm64)multAbund_0.03.9002.tar.gz(r-4.7-x86_64)multAbund_0.03.9002.tar.gz(r-4.6-x86_64)
multAbund_0.03.9002.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
multAbund/json (API)

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

Bug tracker:https://github.com/dsjohnson/multabund/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

2.18 score 3 stars 4 scripts 7 exports 15 dependencies

Last updated from:91880bfd44. Checks:9 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING207
source / vignettesOK258
linux-release-x86_64WARNING202
macos-release-arm64WARNING127
macos-release-x86_64WARNING446
macos-oldrel-arm64WARNING184
macos-oldrel-x86_64WARNING276
windows-develWARNING211
windows-releaseWARNING228
windows-oldrelWARNING188
wasm-releaseOK140

Exports:get_opt_alpha_priormake_data_listmult_abund_normmult_abund_poismult_abund_probitmult_abund_zipview_example

Dependencies:ADGofTestclustercolorspacecopulagsllatticeMatrixmvtnormnumDerivpcaPPpsplineRcppRcppArmadilloRcppProgressstabledist