fmcmc: A friendly MCMC framework

Provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.

Version: 0.4-0
Depends: R (≥ 3.3.0)
Imports: parallel, coda, stats, methods, MASS, Matrix
Suggests: covr, knitr, rmarkdown, mcmc, tinytest, mvtnorm, adaptMCMC
Published: 2020-09-01
Author: George Vega Yon ORCID iD [aut, cre], Paul Marjoram ORCID iD [ctb, ths], National Cancer Institute (NCI) [fnd] (Grant Number 5P01CA196569-02), Fabian Scheipl ORCID iD [rev] (JOSS reviewer)
Maintainer: George Vega Yon <g.vegayon at>
License: MIT + file LICENSE
NeedsCompilation: no
Language: en-US
Citation: fmcmc citation info
Materials: NEWS ChangeLog
CRAN checks: fmcmc results


Reference manual: fmcmc.pdf
Vignettes: User-defined kernels
Workflow with fmcmc
Package source: fmcmc_0.4-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: fmcmc_0.4-0.tgz, r-oldrel: fmcmc_0.4-0.tgz
Old sources: fmcmc archive

Reverse dependencies:

Reverse suggests: ergmito


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