A Framework for Comparing the Performance of MCMC Samplers

adaptive.metropolis.sample | Adaptive Metropolis |

ar.act | Compute the autocorrelation time of a chain |

arms.sample | Adaptive Rejection Metropolis Sampler |

check.dist.gradient | Test a gradient function |

chud | Cholesky Update/Downdate |

compare.samplers | Compare MCMC samplers on distributions |

comparison.plot | Plot the results of compare.samplers |

compounded.sampler | Build a sampler from transition functions |

cov.match.sample | Sample with covariance-matching slice sampling |

dist-class | A class representing a probability distribution |

funnel.dist | Funnel distribution object |

hyperrectangle.sample | Multivariate slice samplers |

make.c.dist | Define a probability distribution object with C log-density |

make.cone.dist | Create a cone distribution object |

make.dist | Define a probability distribution object |

make.gaussian | Gaussian distribution objects |

make.multimodal.dist | Create a distribution object for a random mixture of... |

make.mv.gamma.dist | Create a distribution object for a set of uncorrelated Gamma... |

multivariate.metropolis.sample | Metropolis samplers |

nonadaptive.crumb.sample | Sample with nonadaptive-crumb slice sampling |

oblique.hyperrect.sample | Eigendecomposition-based hyperrectangle method |

raw.symbol | Locate a symbol |

schools.dist | Eight schools distribution object |

shrinking.rank.sample | Sample with shrinking-rank slice sampling |

simulation.result | Summarize one MCMC chain |

stepout.slice.sample | Univariate slice samplers |

twonorm | Euclidean norm of a vector |

univar.eigen.sample | Eigendecomposition-based slice samplers |

wrap.c.sampler | Create an R stub function for a sampler implemented in C |

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