# metaBMA v0.6.1

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## 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.

## Functions in metaBMA

 Name Description meta_random Bayesian Random-Effects Meta-Analysis inclusion Inclusion Bayes Factor bma Bayesian Model Averaging meta_bma Model Averaging for Meta-Analysis plot.meta_pred Plot Predicted Bayes Factors meta_fixed Bayesian Fixed-Effects Meta-Analysis facial_feedback Data Set: Facial Feedback meta_default Defaults for Model Averaging in Meta-Analysis metaBMA-package metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis meta_ordered Meta-Analysis with Order-Constrained Study Effects power_pose Data Set: Power Pose Effect predicted_bf Predicted Bayes Factors for a New Study plot_default Plot Default Priors plot.prior Plot Prior Distribution plot_forest Forest Plot for Meta-Analysis plot_posterior Plot Posterior Distribution towels Data Set: Reuse of Towels in Hotels prior Prior Distribution No Results!