BayesMultMeta |
Interface for the BayesMultMeta class |

bayes_inference |
Summary statistics from a posterior distribution |

duplication_matrix |
Duplication matrix |

MC_ranks |
Computes the ranks within the pooled draws of Markov chains |

plot.BayesMultMeta |
Plot a BayesMultMeta object |

sample_post_nor_jef_marg_mu |
Metropolis-Hastings algorithm for the normal distribution and the Jeffreys prior, where \mathbf{mu} is generated from the marginal posterior. |

sample_post_nor_jef_marg_Psi |
Metropolis-Hastings algorithm for the normal distribution and the Jeffreys prior, where \mathbf{Psi} is generated from the marginal posterior. |

sample_post_nor_ref_marg_mu |
Metropolis-Hastings algorithm for the normal distribution and the Berger and Bernardo reference prior, where \mathbf{mu} is generated from the marginal posterior. |

sample_post_nor_ref_marg_Psi |
Metropolis-Hastings algorithm for the normal distribution and the Berger and Bernardo reference prior, where \mathbf{Psi} is generated from the marginal posterior. |

sample_post_t_jef_marg_mu |
Metropolis-Hastings algorithm for the t-distribution and the Jeffreys prior, where \mathbf{mu} is generated from the marginal posterior. |

sample_post_t_jef_marg_Psi |
Metropolis-Hastings algorithm for the t-distribution and the Jeffreys prior, where \mathbf{Psi} is generated from the marginal posterior. |

sample_post_t_ref_marg_mu |
Metropolis-Hastings algorithm for the t-distribution and Berger and Bernardo reference prior, where \mathbf{mu} is generated from the marginal posterior. |

sample_post_t_ref_marg_Psi |
Metropolis-Hastings algorithm for the t-distribution and Berger and Bernardo reference prior, where \mathbf{Psi} is generated from the marginal posterior. |

split_rank_hatR |
Computes the split-\hat{R} estimate based on the rank normalization |

summary.BayesMultMeta |
Summary statistics from the posterior of a BayesMultMeta class |