metaBMA: 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, <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. For a primer on Bayesian model-averaged meta-analysis,
see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, <doi:10.1177/25152459211031256>).
| Version: |
0.6.9 |
| Depends: |
R (≥ 4.0.0), Rcpp (≥ 1.0.0), methods |
| Imports: |
bridgesampling, coda, LaplacesDemon, logspline, mvtnorm, RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), rstantools (≥
2.3.0) |
| LinkingTo: |
BH (≥ 1.78.0), Rcpp (≥ 1.0.0), RcppEigen (≥ 0.3.3.9.1), RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), StanHeaders (≥
2.26.0) |
| Suggests: |
testthat, knitr, rmarkdown, spelling |
| Published: |
2023-09-13 |
| Author: |
Daniel W. Heck 
[aut, cre],
Quentin F. Gronau [ctb],
Eric-Jan Wagenmakers [ctb],
Indrajeet Patil
[ctb] |
| Maintainer: |
Daniel W. Heck <daniel.heck at uni-marburg.de> |
| License: |
GPL-3 |
| URL: |
https://github.com/danheck/metaBMA,
https://danheck.github.io/metaBMA/ |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU make |
| Language: |
en-US |
| Citation: |
metaBMA citation info |
| Materials: |
NEWS |
| In views: |
MetaAnalysis |
| CRAN checks: |
metaBMA results |
Documentation:
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