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, <https://doi.org/10.1214/12-AOS1048>, function morph.metrop), which achieves geometric ergodicity by change of variable.

Version: 0.9-5
Depends: R (≥ 3.0.2)
Imports: stats, compiler
Suggests: xtable, Iso
Published: 2017-04-16
Author: Charles J. Geyer and Leif T. Johnson
Maintainer: Charles J. Geyer <charlie at stat.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

Downloads:

Reference manual: mcmc.pdf
Vignettes: Bayes Factors via Serial Tempering
Debugging MCMC Code
MCMC Example
MCMC Morph Example
Package source: mcmc_0.9-5.tar.gz
Windows binaries: r-devel: mcmc_0.9-5.zip, r-release: mcmc_0.9-5.zip, r-oldrel: mcmc_0.9-5.zip
OS X El Capitan binaries: r-release: mcmc_0.9-5.tgz
OS X Mavericks binaries: r-oldrel: mcmc_0.9-5.tgz
Old sources: mcmc archive

Reverse dependencies:

Reverse depends: ltbayes
Reverse imports: MCMCpack, nse, prefeR, ReliabilityTheory, sizeMat, TBSSurvival
Reverse suggests: ConnMatTools, pse

Linking:

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