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) (<doi:10.48550/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>).

Version: 0.1.1
Imports: assertthat, Rdpack
Suggests: mvmeta, gplots, testthat
Published: 2022-06-09
Author: Olha Bodnar ORCID iD [aut], Taras Bodnar ORCID iD [aut], Erik Thorsén ORCID iD [aut, cre]
Maintainer: Erik Thorsén <erik.thorsen at math.su.se>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
In views: MetaAnalysis
CRAN checks: BayesMultMeta results

Documentation:

Reference manual: BayesMultMeta.pdf

Downloads:

Package source: BayesMultMeta_0.1.1.tar.gz
Windows binaries: r-devel: BayesMultMeta_0.1.1.zip, r-release: BayesMultMeta_0.1.1.zip, r-oldrel: BayesMultMeta_0.1.1.zip
macOS binaries: r-release (arm64): BayesMultMeta_0.1.1.tgz, r-oldrel (arm64): BayesMultMeta_0.1.1.tgz, r-release (x86_64): BayesMultMeta_0.1.1.tgz
Old sources: BayesMultMeta archive

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