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>).
