Package: bayesMRM 2.4.0

Man-Suk Oh

bayesMRM: Bayesian Multivariate Receptor Modeling

Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) <doi:10.1016/j.chemolab.2015.08.021>.The package uses 'JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters.

Authors:Man-Suk Oh [aut, cre], Eun-Kyung Lee [aut], Eun Sug Park [aut]

bayesMRM_2.4.0.tar.gz
bayesMRM_2.4.0.zip(r-4.7-any)bayesMRM_2.4.0.zip(r-4.6-any)
bayesMRM_2.4.0.tar.gz(r-4.7-any)bayesMRM_2.4.0.tar.gz(r-4.6-any)
bayesMRM_2.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
bayesMRM/json (API)

# Install 'bayesMRM' in R:
install.packages('bayesMRM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • Elpaso - PM2.5 speciation data from El Paso, Texas, USA.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

jagscpp

1.00 score 261 downloads 5 exports 55 dependencies

Last updated from:1282e61a92. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develOK149
source / vignettesOK206
linux-releaseOK116
windows-develOK71
windows-releaseOK70
wasm-releaseOK137

Exports:bayesMRMAppbmrmconvdiag_bmrmpcplottrace_ACF_plot

Dependencies:base64encbslibcachemclicodacommonmarkcpp11digestevaluatefarverfastmapfontawesomefsggplot2gluegridExtragtablehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelifecyclemagrittrmemoisemimeotelpromisesR6rappdirsRColorBrewerRcpprglrjagsrlangrmarkdownS7sassscalesshinyshinythemessourcetoolstinytexvctrsviridisLitewithrxfunxtableyaml