metaBMA (0.6.2)

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Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis.

https://github.com/danheck/metaBMA
http://cran.r-project.org/web/packages/metaBMA

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.

Maintainer: Daniel Heck
Author(s): Daniel W. Heck [aut, cre] (<https://orcid.org/0000-0002-6302-9252>), Quentin F. Gronau [ctb]

License: GPL-3

Uses: bridgesampling, coda, LaplacesDemon, logspline, mvtnorm, Rcpp, rstan, rstantools, knitr, testthat

Released about 1 month ago.


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