mcmc (0.9-7)

0 users

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.

Maintainer: Charles J. Geyer
Author(s): Charles J. Geyer <> and Leif T. Johnson <>

License: MIT + file LICENSE

Uses: Iso, xtable
Reverse depends: ltbayes, TBSSurvival, VarEff
Reverse suggests: ConnMatTools, fmcmc, MSGARCH, pse, SamplerCompare

Released 8 days ago.

13 previous versions



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of mcmc yet. Want to be the first? Write one now.

Related packages: BACCO, BMA, BayHaz, BayesTree, BayesValidate, Bolstad, EbayesThresh, HI, Hmisc, LearnBayes, MCMCpack, MNP, R2WinBUGS, Runuran, arm, bayesm, bayesmix, bnlearn, boa, bqtl(20 best matches, based on common tags.)

Search for mcmc on google, google scholar, r-help, r-devel.

Visit mcmc on R Graphical Manual.