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, ). 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, ).


News

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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install.packages("metaBMA")

0.6.7 by Daniel W. Heck, 6 months ago


https://github.com/danheck/metaBMA


Browse source code at https://github.com/cran/metaBMA


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


Documentation:   PDF Manual  


Task views: Meta-Analysis


GPL-3 license


Imports bridgesampling, coda, LaplacesDemon, logspline, mvtnorm, RcppParallel, rstan, rstantools

Depends on Rcpp, methods

Suggests testthat, knitr, rmarkdown, spelling

Linking to BH, Rcpp, RcppEigen, RcppParallel, rstan, StanHeaders

System requirements: GNU make


Suggested by RoBMA, ggstatsplot, insight, parameters, statsExpressions.


See at CRAN