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 + file LICENSE
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.pdf
Vignettes:	Bayes Factors via Serial Tempering Debugging MCMC Code MCMC Example MCMC Morph Example

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, nse, prefeR, ReliabilityTheory
Reverse suggests:	ConnMatTools, fmcmc, MSGARCH

Linking:

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