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: 0.5-2 Depends: R (≥ 3.3.0) Imports: parallel, coda, stats, methods, MASS, Matrix Suggests: covr, knitr, rmarkdown, mcmc, tinytest, mvtnorm Published: 2023-08-29 DOI: 10.32614/CRAN.package.fmcmc Author: George Vega Yon [aut, cre], Paul Marjoram [ctb, ths], National Cancer Institute (NCI) [fnd] (Grant Number 5P01CA196569-02), Fabian Scheipl [rev] (what: JOSS reviewer) Maintainer: George Vega Yon <g.vegayon at gmail.com> BugReports: https://github.com/USCbiostats/fmcmc/issues License: MIT + file LICENSE URL: https://github.com/USCbiostats/fmcmc NeedsCompilation: no Language: en-US Citation: fmcmc citation info Materials: NEWS, ChangeLog CRAN checks: fmcmc results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=fmcmc to link to this page.
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