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, , function morph.metrop), which achieves geometric ergodicity by change of variable.


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Reference manual

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install.packages("mcmc")

0.9-6 by Charles J. Geyer, 3 months ago


http://www.stat.umn.edu/geyer/mcmc/, https://github.com/cjgeyer/mcmc


Browse source code at https://github.com/cran/mcmc


Authors: Charles J. Geyer <[email protected]> and Leif T. Johnson <[email protected]>


Documentation:   PDF Manual  


Task views: Bayesian Inference


MIT + file LICENSE license


Imports stats

Suggests xtable, Iso


Imported by MCMCpack, ReliabilityTheory, TBSSurvival, nse, prefeR, sizeMat.

Depended on by ltbayes.

Suggested by ConnMatTools, MSGARCH, pse.


See at CRAN