Bayesian Multivariate Meta-Analysis

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Documentation for package ‘BayesMultMeta’ version 0.1.1

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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