# Bayesian Multivariate Meta-Analysis

## Help Pages

 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