c(
person("František", "Bartoš",     role = c("aut", "cre"),
email   = "f.bartos96@gmail.com", comment = c(ORCID = "0000-0002-0018-5573")),
person("Maximilian", "Maier",     role = "aut",
email   = "maximilianmaier0401@gmail.com", comment = c(ORCID = "0000-0002-9873-6096")),
person("Eric-Jan", "Wagenmakers", role = "ths",
comment = c(ORCID = "0000-0003-1596-1034")),
person("Joris", "Goosen",         role = "ctb")
)
A framework for estimating ensembles of meta-analytic models
(assuming either presence or absence of the effect, heterogeneity, and
publication bias) and using Bayesian model averaging to combine them. The
ensembles use Bayes Factors to test for the presence or absence of the
individual components (e.g., effect vs. no effect) and model-averages
parameter estimates based on posterior model probabilities
(Maier, Bartoš & Wagenmakers, 2020, <doi:10.31234/osf.io/u4cns>). The user can
define a wide range of non-informative or informative priors for the
effect size, heterogeneity, and weight functions. The package provides
convenient functions for summary, visualizations, and fit diagnostics.
runjags, bridgesampling, rjags, coda, psych, stats, graphics,
extraDistr, scales, DPQ, callr, Rdpack
ggplot2, rstan, metaBMA, testthat, vdiffr, knitr, rmarkdown
František Bartoš [aut, cre] (<https://orcid.org/0000-0002-0018-5573>),
Maximilian Maier [aut] (<https://orcid.org/0000-0002-9873-6096>),
Eric-Jan Wagenmakers [ths] (<https://orcid.org/0000-0003-1596-1034>),
Joris Goosen [ctb]

A framework for estimating ensembles of meta-analytic models
(assuming either presence or absence of the effect, heterogeneity, and
publication bias) and using Bayesian model averaging to combine them. The
ensembles use Bayes factors to test for the presence or absence of the
individual components (e.g., effect vs. no effect) and model-averages
parameter estimates based on posterior model probabilities
(Maier, Bartoš & Wagenmakers, 2020, <doi:10.31234/osf.io/u4cns>). The user can
define a wide range of non-informative or informative priors for the
effect size, heterogeneity, and weight functions. The package provides
convenient functions for summary, visualizations, and fit diagnostics.