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mcmc: Markov Chain Monte Carlo

Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, 2012, <doi:10.1214/12-AOS1048>, function morph.metrop), which achieves geometric ergodicity by change of variable.

Version:0.9-8
Depends:R (≥ 3.6.0)
Imports:stats
Suggests:xtable,Iso
Published:2023-11-16
DOI:10.32614/CRAN.package.mcmc
Author:Charles J. Geyer and Leif T. Johnson
Maintainer:Charles J. Geyer <geyer at umn.edu>
License:MIT + fileLICENSE
URL:http://www.stat.umn.edu/geyer/mcmc/,https://github.com/cjgeyer/mcmc
NeedsCompilation:yes
Materials:ChangeLog
In views:Bayesian
CRAN checks:mcmc results

Documentation:

Reference manual:mcmc.html ,mcmc.pdf
Vignettes:Bayes Factors via Serial Tempering (source,R code)
Debugging MCMC Code (source,R code)
MCMC Example (source,R code)
MCMC Morph Example (source,R code)

Downloads:

Package source: mcmc_0.9-8.tar.gz
Windows binaries: r-devel:mcmc_0.9-8.zip, r-release:mcmc_0.9-8.zip, r-oldrel:mcmc_0.9-8.zip
macOS binaries: r-release (arm64):mcmc_0.9-8.tgz, r-oldrel (arm64):mcmc_0.9-8.tgz, r-release (x86_64):mcmc_0.9-8.tgz, r-oldrel (x86_64):mcmc_0.9-8.tgz
Old sources: mcmc archive

Reverse dependencies:

Reverse imports:geommc,MCMCpack,prefeR,ReliabilityTheory
Reverse suggests:ConnMatTools,ebnm,fmcmc,MSGARCH

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=mcmcto link to this page.


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