Package: BayesMultMeta 0.1.1

BayesMultMeta: Bayesian Multivariate Meta-Analysis

Objective Bayesian inference procedures for the parameters of the multivariate random effects model with application to multivariate meta-analysis. The posterior for the model parameters, namely the overall mean vector and the between-study covariance matrix, are assessed by constructing Markov chains based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar (2021) (<arxiv:2104.02105>). The Metropolis-Hastings algorithm is designed under the assumption of the normal distribution and the t-distribution when the Berger and Bernardo reference prior and the Jeffreys prior are assigned to the model parameters. Convergence properties of the generated Markov chains are investigated by the rank plots and the split hat-R estimate based on the rank normalization, which are proposed in Vehtari et al. (2021) (<doi:10.1214/20-BA1221>).

Authors:Olha Bodnar [aut], Taras Bodnar [aut], Erik Thorsén [aut, cre]

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BayesMultMeta/json (API)

# Install 'BayesMultMeta' in R:
install.packages('BayesMultMeta', repos = c('https://ethorsn.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 212 downloads 15 exports 3 dependencies

Last updated 2 years agofrom:7672c31e20. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 22 2024
R-4.5-winOKOct 22 2024
R-4.5-linuxOKOct 22 2024
R-4.4-winOKOct 22 2024
R-4.4-macOKOct 22 2024
R-4.3-winOKOct 22 2024
R-4.3-macOKOct 22 2024

Exports:bayes_inferenceBayesMultMetaduplication_matrixMC_ranksplot.BayesMultMetasample_post_nor_jef_marg_musample_post_nor_jef_marg_Psisample_post_nor_ref_marg_musample_post_nor_ref_marg_Psisample_post_t_jef_marg_musample_post_t_jef_marg_Psisample_post_t_ref_marg_musample_post_t_ref_marg_Psisplit_rank_hatRsummary.BayesMultMeta

Dependencies:assertthatrbibutilsRdpack

Readme and manuals

Help Manual

Help pageTopics
Summary statistics from a posterior distributionbayes_inference
Interface for the BayesMultMeta classBayesMultMeta
Duplication matrixduplication_matrix
Computes the ranks within the pooled draws of Markov chainsMC_ranks
Plot a BayesMultMeta objectplot.BayesMultMeta
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_mu
Metropolis-Hastings algorithm for the normal distribution and the Jeffreys prior, where \mathbf{Psi} is generated from the marginal posterior.sample_post_nor_jef_marg_Psi
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_mu
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_nor_ref_marg_Psi
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_mu
Metropolis-Hastings algorithm for the t-distribution and the Jeffreys prior, where \mathbf{Psi} is generated from the marginal posterior.sample_post_t_jef_marg_Psi
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_mu
Metropolis-Hastings algorithm for the t-distribution and Berger and Bernardo reference prior, where \mathbf{Psi} is generated from the marginal posterior.sample_post_t_ref_marg_Psi
Computes the split-\hat{R} estimate based on the rank normalizationsplit_rank_hatR
Summary statistics from the posterior of a BayesMultMeta classsummary.BayesMultMeta