fmcmc
2023
bayesian
mcmc
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing the Metropolis-Hastings algorithm in a modular way, allowing users to specify an automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing.
ActivePackage2023CRAN0.5-2downloads35kstars16Cited by8
Citation counts from OpenAlex; downloads from CRAN. Updated 2026-08-15.
A friendly MCMC framework
About
Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing the Metropolis-Hastings algorithm in a modular way, allowing users to specify an automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Among the methods included are Haario (2001) Adaptive Metropolis, Vihola (2012) Robust Adaptive Metropolis, and Thawornwattana et al. (2018) Mirror transition kernels.
Cite
@Manual{fmcmc,
title = {fmcmc: A friendly MCMC framework},
author = {{Vega Yon}, {George G.}},
year = {2023},
doi = {10.32614/CRAN.package.fmcmc},
url = {https://CRAN.R-project.org/package=fmcmc},
note = {R package version 0.5-2},
}