Package: rjags 4-17

rjags: Bayesian Graphical Models using MCMC

Interface to the JAGS MCMC library.

Authors:Martyn Plummer [aut, cre], Alexey Stukalov [ctb], Matt Denwood [ctb]

rjags_4-17.tar.gz
rjags_4-17.zip(r-4.7)rjags_4-17.zip(r-4.6)rjags_4-17.zip(r-4.5)
rjags_4-17.tgz(r-4.6-x86_64)rjags_4-17.tgz(r-4.6-arm64)rjags_4-17.tgz(r-4.5-x86_64)rjags_4-17.tgz(r-4.5-arm64)
rjags_4-17.tar.gz(r-4.7-arm64)rjags_4-17.tar.gz(r-4.7-x86_64)rjags_4-17.tar.gz(r-4.6-arm64)rjags_4-17.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
rjags/json (API)

# Install 'rjags' in R:
install.packages('rjags', repos = c('https://martynplummer.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:
  • LINE - Linear regression example

On CRAN:

Conda:

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

jagscpp

9.78 score 9 stars 161 packages 5.8k scripts 24k downloads 260 mentions 17 exports 2 dependencies

Last updated from:59ec6d347d. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK126
linux-devel-x86_64OK105
source / vignettesOK173
linux-release-arm64OK140
linux-release-x86_64OK105
macos-release-arm64OK135
macos-release-x86_64OK203
macos-oldrel-arm64OK127
macos-oldrel-x86_64OK278
windows-develOK92
windows-releaseOK114
windows-oldrelOK89
wasm-releaseFAIL102

Exports:adaptcoda.samplesdic.samplesdiffdicjags.modeljags.samplesjags.versionlist.factorieslist.moduleslist.samplersload.moduleparallel.seedsread.bugsdataread.dataread.jagsdataset.factoryunload.module

Dependencies:codalattice

Readme and manuals

Help Manual

Help pageTopics
Bayesian graphical models using MCMCrjags-package rjags
Adaptive phase for JAGS modelsadapt
Generate posterior samples in mcmc.list formatcoda.samples
Advanced control over JAGSlist.factories set.factory
Generate penalized deviance samplesdic dic.samples
Differences in penalized deviancediffdic
Create a JAGS model objectjags.model
Dynamically load JAGS moduleslist.modules load.module unload.module
Functions for manipulating jags model objectscoef.jags list.samplers variable.names.jags
Generate posterior samplesjags.samples
JAGS versionJAGS.version jags.version
Linear regression exampleLINE
Objects for representing MCMC outputas.mcmc.list.mcarray mcarray.object print.mcarray summary.mcarray
Get initial values for parallel RNGsparallel.seeds
Read data files for jags modelsread.bugsdata read.jagsdata
Deprecated Functions in the rjags packageread.data rjags-deprecated
Update jags modelsupdate.jags