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, Annals of Statistics, 2012, function morph.metrop), which achieves geometric ergodicity by change of variable.

Version: | 0.9-4 |

Depends: | R (≥ 2.10.0) |

Imports: | stats |

Suggests: | xtable, Iso |

Published: | 2015-07-17 |

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 |

Reference manual: | mcmc.pdf |

Vignettes: |
Bayes Factors via Serial Tempering Debugging MCMC Code MCMC Example MCMC Morph Example |

Package source: | mcmc_0.9-4.tar.gz |

Windows binaries: | r-devel: mcmc_0.9-4.zip, r-release: mcmc_0.9-4.zip, r-oldrel: mcmc_0.9-4.zip |

OS X Snow Leopard binaries: | r-release: mcmc_0.9-4.tgz, r-oldrel: mcmc_0.9-3.tgz |

OS X Mavericks binaries: | r-release: mcmc_0.9-4.tgz |

Old sources: | mcmc archive |

Reverse depends: | ltbayes |

Reverse imports: | ReliabilityTheory, TBSSurvival |

Reverse suggests: | pse |