Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

Computes the posterior model probabilities for four 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, ). The user can define a wide range of noninformative or informative priors for the mean effect size and the heterogeneity coefficient. Funding for this research was provided by the Berkeley Initiative for Transparency in the Social Sciences, a program of the Center for Effective Global Action (CEGA), with support from the Laura and John Arnold Foundation.


metaBMA 0.3.9

  • Updated citation for CRAN
  • Added examples for meta_bma() and meta_random()
  • Minor bug fixes

metaBMA 0.3.8

  • Data sets 'power_pose' and 'power_pose_unfamiliar' added
  • Data set 'facial_feedback' added
  • More informative description file
  • Requirements for CRAN

metaBMA 0.3.0

  • First stable version
  • High-level functions meta_bma() and meta_default() perform model averaging for standard models (fixed, random + H0, H1)
  • Plotting functions for averaged/random-effects/fixed-effects meta-analysis via plot_forest() and plot_posterior()
  • Meta-analysis models are fitted by meta_fixed() and meta_random()
  • Effect estimates of fitted meta-analysis models can be averaged by bma()
  • Inclusion Bayes factor are computed by inclusion()
  • User-specified and default prior functions are specified via prior() [can be plottet via plot(prior)]

Reference manual

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0.3.9 by Daniel W. Heck, 2 years ago

Browse source code at

Authors: Daniel W. Heck [aut, cre] , Quentin F. Gronau [aut] , Eric-Jan Wagenmakers [aut]

Documentation:   PDF Manual  

Task views: Meta-Analysis

GPL-3 license

Imports mvtnorm, logspline, coda, runjags, LaplacesDemon

Suggests testthat, knitr

System requirements: JAGS (

See at CRAN