Package: runjags 2.2.2-4

Matthew Denwood

runjags: Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS

User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.

Authors:Matthew Denwood [aut, cre], Martyn Plummer [cph]

runjags_2.2.2-4.tar.gz
runjags_2.2.2-4.tar.gz(r-4.5-noble)runjags_2.2.2-4.tar.gz(r-4.4-noble)
runjags.pdf |runjags.html
runjags/json (API)

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

Peer review:

Bug tracker:https://github.com/ku-awdc/runjags/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

jagscpp

9.40 score 3 stars 36 packages 1.2k scripts 6.5k downloads 39 mentions 59 exports 2 dependencies

Last updated 9 months agofrom:0d78356088. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linux-x86_64OKDec 06 2024

Exports:add.summaryas.jagsas.runjagsaskautoextend.jagsautoextend.JAGSautorun.jagsautorun.JAGScleanup.jagscleanup.JAGScombine.jagscombine.JAGScombine.mcmccombine.MCMCcontrasts.mcmccontrasts.MCMCdivide.jagsdivide.JAGSdrop.kdrop.k.jagsdrop.k.JAGSdump.formatextend.jagsextend.JAGSextractextract.runjagsfailed.jagsfailed.JAGSfailedjagsfindjagsfindJAGSis.runjagslist.formatload.runjagsmoduleload.runJAGSmodulenew_uniqueprec2sdread.jagsfileread.JAGSfileread.winbugsread.WinBUGSresults.jagsresults.JAGSrun.jagsrun.JAGSrun.jags.studyrun.JAGS.studyrunjags.getOptionrunjags.optionstemplate_huiwaltertemplate.jagstemplate.JAGStestjagstestJAGStimestringunload.runjagsmoduleunload.runJAGSmodulewrite.jagsfilewrite.JAGSfile

Dependencies:codalattice

A quick-start guide to running models in JAGS

Rendered fromquickjags.Rmdusingknitr::knitron Dec 06 2024.

Last update: 2024-03-11
Started: 2015-04-12

Using the runjags package

Rendered fromUserGuide.Rtexusingutils::Sweaveon Dec 06 2024.

Last update: 2022-03-09
Started: 2015-04-12

Readme and manuals

Help Manual

Help pageTopics
Summary statistics and plot methods for runjags class objectsadd.summary plot.runjags plot.runjagsplots print.runjags print.runjagsplots summary.runjags
Obtain Input from User With Error Handlingask
Run or extend a user-specified Bayesian MCMC model in JAGS with automatically calculated run-length and convergence diagnosticsautoextend.JAGS autoextend.jags autorun.JAGS autorun.jags
Combining and dividing runjags and MCMC objectscombine.JAGS combine.jags combine.MCMC combine.mcmc divide.JAGS divide.jags
Conversion Between a Named List and a Character String in the R Dump Formatdump.format dump.list.format list.format
Extract peripheral information from runjags objectsdic.runjags extract extract.jags extract.runjags
Attempt to Locate a JAGS InstallfindJAGS findjags
Load the internal JAGS module provided by runjagsload.runJAGSmodule load.runjagsmodule unload.runJAGSmodule unload.runjagsmodule
Mutate functions to be used with runjags summary methodscontrasts.MCMC contrasts.mcmc mutate.functions prec2sd
Create a Unique Filenamenew_unique
Extract Any Models, Data, Monitored Variables or Initial Values As Character Vectors from a JAGS or WinBUGS Format Textfileread.JAGSfile read.jagsfile read.WinBUGS read.winbugs
Importing of saved JAGS simulations with partial error recoveryresults.JAGS results.jags
Run or extend a user-specified Bayesian MCMC model in JAGS from within Rextend.JAGS extend.jags run.JAGS run.jags
Drop-k and simulated dataset studies using JAGSdrop.k drop.k.JAGS drop.k.jags run.JAGS.study run.jags.study
The runjags class and available S3 methodsas.jags as.jags.default as.jags.runjags as.mcmc.list.runjags as.mcmc.runjags as.runjags as.runjags.default as.runjags.jags cleanup.JAGS cleanup.jags failed.JAGS failed.jags failedjags fitted.runjags is.runjags predict.runjags residuals.runjags runjags-class runjagsclass runjagsstudy-class runjagsstudyclass
Options for the runjags packagerunJAGS.getOption runjags.getOption runJAGS.options runjags.options
Print methods for runjags helper classesplot.runjagsstudy print.crosscorrstats print.dicstats print.failedjags print.gelman.with.target print.gelmanwithtarget print.mcsestats print.rjagsoutput print.runjagsbginfo print.runjagsdata print.runjagsinits print.runjagsmodel print.runjagsoutput print.runjagsstudy runjags.printmethods summary.runjagsstudy
Create a Hui-Walter model based on paired test data for an arbitrary number of tests and populationstemplate_huiwalter
Generate a generalised linear mixed model (GLMM) specification in JAGStemplate.JAGS template.jags
Analyse the System to Check That JAGS Is InstalledtestJAGS testjags
Calculate the Elapsed Time in Sensible Unitstimestring
Write a complete JAGS model to a text filewrite.JAGSfile write.jagsfile