Computes the posterior model probabilities for standard metaanalysis models (null model vs. alternative model assuming either fixed or randomeffects, 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 fixedeffect 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 noninformative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using precompiled Stan models, metaanalysis with continuous and discrete moderators with JeffreysZellnerSiow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random and fixedeffects metaanalysis with and without moderators. For a primer on Bayesian modelaveraged metaanalysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2020, <doi:10.31234/osf.io/97qup>).
Package details 


Author  Daniel W. Heck [aut, cre] (<https://orcid.org/0000000263029252>), Quentin F. Gronau [ctb], EricJan Wagenmakers [ctb], Indrajeet Patil [ctb] (<https://orcid.org/0000000319956531>) 
Maintainer  Daniel W. Heck <dheck@unimarburg.de> 
License  GPL3 
Version  0.6.7 
URL  https://github.com/danheck/metaBMA 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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