Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis
Bayesian Model Averaging
Inclusion Bayes Factor
Model Averaging for Meta-Analysis
Defaults for Model Averaging in Meta-Analysis
Bayesian Fixed-Effects Meta-Analysis
Meta-Analysis with Order-Constrained Study Effects
Bayesian Random-Effects Meta-Analysis
Sensitivity Analysis for Bayesian Meta-Analysis
metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-An...
Plot Predicted Bayes Factors
Plot Sensitivity Analysis for Meta-Analysis
Plot Prior Distribution
Plot Default Priors
Forest Plot for Meta-Analysis
Plot Posterior Distribution
Predicted Bayes Factors for a New Study
Prior Distribution
Transformation of Effect Sizes
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>).