MCMCpack Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
MCMCpack contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return coda mcmc objects that can then be summarized using the coda package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.
NetworkChange Jong Hee Park and Yunkyu Sohn
Network changepoint analysis for undirected network data. The package implements a hidden Markov multilinear tenstor regression model (Park and Sohn, 2017). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.
CRAN Task View: Bayesian Inference Maintainer
Applied researchers interested in Bayesian statistics are increasingly attracted to R because of the ease of which one can code algorithms to sample from posterior distributions as well as the significant number of packages contributed to the Comprehensive R Archive Network (CRAN) that provide tools for Bayesian inference. This task view catalogs these tools. In this task view, we divide those packages into four groups based on the scope and focus of the packages. We first review R packages that provide Bayesian estimation tools for a wide range of models. We then discuss packages that address specific Bayesian models or specialized methods in Bayesian statistics. This is followed by a description of packages used for post-estimation analysis. Finally, we review packages that link R to other Bayesian sampling engines such as JAGS, OpenBUGS, WinBUGS, and Stan.