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>).
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