Bayesian Model Averaging for Random and Fixed Effects
Meta-Analysis
c(person("Daniel W.","Heck",email="heck@uni-mannheim.de",role=c("aut","cre")),
person("Quentin F.", "Gronau", email="quentingronau@web.de", role = c("ctb")),
person("Eric-Jan", "Wagenmakers", email="ej.wagenmakers@gmail.com", role = c("ctb")))
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, <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. 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.
mvtnorm, logspline, coda, runjags, LaplacesDemon
Daniel W. Heck [aut, cre],
Quentin F. Gronau [ctb],
Eric-Jan Wagenmakers [ctb]

c(person("Daniel W.","Heck",email="heck@uni-mannheim.de",role=c("aut","cre")),
person("Quentin F.", "Gronau", email="quentingronau@web.de", role = c("aut")),
person("Eric-Jan", "Wagenmakers", email="ej.wagenmakers@gmail.com", role = c("aut")))
Daniel W. Heck [aut, cre],
Quentin F. Gronau [aut],
Eric-Jan Wagenmakers [aut]

c(person("Daniel W.","Heck",email="heck@uni-mannheim.de",role=c("aut","cre")),
person("Quentin F.", "Gronau", email="quentingronau@web.de", role = c("ctb")))
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.
mvtnorm, logspline, coda, LaplacesDemon, rstan (>= 2.18.1),
rstantools (>= 1.5.1), bridgesampling
Daniel W. Heck [aut, cre],
Quentin F. Gronau [ctb]