This package is considered a duplicate. The official version of this package is found at:https://martynplummer.r-universe.dev/rjags
Package: rjags 4-17
Martyn Plummer
rjags: Bayesian Graphical Models using MCMC
Interface to the JAGS MCMC library.
Authors:
rjags_4-17.tar.gz
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✨
card.svg |card.png
rjags/json (API)
| # Install 'rjags' in R: |
| install.packages('rjags', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Uses libs:
- jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
- c++– GNU Standard C++ Library v3
Datasets:
- LINE - Linear regression example
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:59ec6d347d. Checks:5 OK, 1 FAIL. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 118 | ||
| linux-devel-x86_64 | OK | 137 | ||
| source / vignettes | OK | 153 | ||
| linux-release-arm64 | OK | 148 | ||
| linux-release-x86_64 | OK | 142 | ||
| wasm-release | FAIL | 108 |
Exports:adaptcoda.samplesdic.samplesdiffdicjags.modeljags.samplesjags.versionlist.factorieslist.moduleslist.samplersload.moduleparallel.seedsread.bugsdataread.dataread.jagsdataset.factoryunload.module
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian graphical models using MCMC | rjags-package rjags |
| Adaptive phase for JAGS models | adapt |
| Generate posterior samples in mcmc.list format | coda.samples |
| Advanced control over JAGS | list.factories set.factory |
| Generate penalized deviance samples | dic dic.samples |
| Differences in penalized deviance | diffdic |
| Create a JAGS model object | jags.model |
| Dynamically load JAGS modules | list.modules load.module unload.module |
| Functions for manipulating jags model objects | coef.jags list.samplers variable.names.jags |
| Generate posterior samples | jags.samples |
| JAGS version | JAGS.version jags.version |
| Linear regression example | LINE |
| Objects for representing MCMC output | as.mcmc.list.mcarray mcarray.object print.mcarray summary.mcarray |
| Get initial values for parallel RNGs | parallel.seeds |
| Read data files for jags models | read.bugsdata read.jagsdata |
| Deprecated Functions in the rjags package | read.data rjags-deprecated |
| Update jags models | update.jags |
