R2jags: Using R to Run 'JAGS'

Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.

Version: 0.6-1
Depends: R (≥ 2.14.0), rjags (≥ 3-3)
Imports: abind, coda (≥ 0.13), graphics, grDevices, methods, R2WinBUGS, parallel, stats, utils
Published: 2020-04-27
Author: Yu-Sung Su, Masanao Yajima,
Maintainer: Yu-Sung Su <suyusung at tsinghua.edu.cn>
BugReports: https://github.com/suyusung/R2jags/issues/
License: GPL (> 2)
NeedsCompilation: no
SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net)
Materials: README ChangeLog
In views: Bayesian
CRAN checks: R2jags results


Reference manual: R2jags.pdf
Package source: R2jags_0.6-1.tar.gz
Windows binaries: r-devel: R2jags_0.6-1.zip, r-release: R2jags_0.6-1.zip, r-oldrel: R2jags_0.6-1.zip
macOS binaries: r-release: R2jags_0.6-1.tgz, r-oldrel: R2jags_0.6-1.tgz
Old sources: R2jags archive

Reverse dependencies:

Reverse depends: bmeta, CCTpack, eivtools, hbbr, HETOP, IUPS, miscF, simmr
Reverse imports: agRee, bamdit, BayesPostEst, boral, BTSPAS, HiLDA, jarbes, MBNMAdose, MBNMAtime, missingHE, MixSIAR, SeqFeatR
Reverse suggests: AICcmodavg, BCEA, bridgesampling, broom.mixed, emdbook, ftsa, gap, LakeMetabolizer


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