Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis R package metaBMA version 0.6.9 CRAN Task View: MetaAnalysis (https://CRAN.R-project.org/view=MetaAnalysis) Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, <doi:10.1177/25152459211031256>). Software Depends: R (>= 4.0.0), Rcpp (>= 1.0.0), methods Imports: bridgesampling, coda, LaplacesDemon, logspline, mvtnorm, RcppParallel (>= 5.0.1), rstan (>= 2.26.0), rstantools (>= 2.3.0) LinkingTo: BH (>= 1.78.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.9.1), RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0) Suggests: testthat, knitr, rmarkdown, spelling Daniel W. Heck <daniel.heck@uni-marburg.de> Comprehensive R Archive Network (CRAN) Daniel W. Heck [aut, cre] (ORCID: <https://orcid.org/0000-0002-6302-9252>), Quentin F. Gronau [ctb], Eric-Jan Wagenmakers [ctb], Indrajeet Patil [ctb] (ORCID: <https://orcid.org/0000-0003-1995-6531>) GPL-3 2023-09-13 application/tgz https://CRAN.R-project.org/package=metaBMA doi:10.32614/CRAN.package.metaBMA en-US