R package metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis. 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 (2020, <doi:10.31234/osf.io/97qup>).
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References in zbMATH (referenced in 2 articles )
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- Hyemin Han: BayesFactorFMRI: Implementing Bayesian Second-Level fMRI Analysis with Multiple Comparison Correction and Bayesian Meta-Analysis of fMRI Images with Multiprocessing (2021) not zbMATH
- Indrajeet Patil: statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details (2021) not zbMATH