Perform Bayesian modelling in R using JAGS.

🚀 This is the development version of the R package R2jags (currently on version 0.8-10). The stable version is now release 0.8-9, on CRAN.

• Overview
• Features
• Installation

R2jags provides a set of wrapper functions to implement Bayesian analysis using Markov Chain Monte Carlo (specifically, Gibbs sampling) in JAGS. Some major features include monitoring convergence of a MCMC model using Gelman-Rubin Potential Scale Reduction (“Rhat”) statistics, automatically running a MCMC model until it converges, and implementing parallel processing of a MCMC model for multiple chains.

Main features of R2jags include:

• Wrapping functions from rjags and R2WinBUGS to run JAGS in the background and produce a MCMC analysis of a given model.
• Summary statistics and tables
• Convergence and auto-correlation calculations and plots

Install the released version from CRAN with

install.packages("R2jags")

The development version (in this repo, which can be updated more quickly and more often than the stable one) can be installed from r-universe.dev, using the following command

install.packages(
  'R2jags', 
  repos = c('https://giabaio.r-universe.dev', 'https://cloud.r-project.org')
)

Alternatively, you can install the development version using remotes, with the following command.

install.packages("remotes")
remotes::install_github("giabaio/R2jags",ref="devel")

NB: On Windows machines, you need to install a few dependencies, including Rtools first, e.g. by running

pkgs <- c("MASS", "Rtools", "remotes")
repos <- "https://cran.rstudio.com"
install.packages(pkgs, repos = repos, dependencies = "Depends")

before installing the package using remotes.

Please submit contributions through Pull Requests, following the contributing guidelines. To report issues and/or seek support, please file a new ticket in the issue tracker.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.