RoBMA: Robust Bayesian Meta-Analyses
A framework for estimating ensembles of meta-analytic and meta-regression models
(assuming either presence or absence of the effect, heterogeneity,
publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to
combine the competing meta-analytic models into a model ensemble, weights
the posterior parameter distributions based on posterior model probabilities
and uses Bayes factors to test for the presence or absence of the
individual components (e.g., effect vs. no effect; Bartoš et al., 2022,
<doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022,
<doi:10.1037/met0000405>). Users can define a wide range of prior distributions for +
the effect size, heterogeneity, publication bias (including selection models and PET-PEESE),
and moderator components. The package provides convenient functions for summary, visualizations, and
fit diagnostics.
| Version: |
3.4.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
BayesTools (≥ 0.2.18), runjags, rjags, stats, graphics, mvtnorm, scales, Rdpack, rlang, coda, ggplot2 |
| LinkingTo: |
mvtnorm |
| Suggests: |
parallel, metaBMA, metafor, weightr, lme4, fixest, emmeans, metadat, testthat, vdiffr, knitr, rmarkdown, covr |
| Published: |
2025-02-04 |
| DOI: |
10.32614/CRAN.package.RoBMA |
| Author: |
František Bartoš 
[aut, cre],
Maximilian Maier
[aut],
Eric-Jan Wagenmakers
[ths],
Joris Goosen [ctb],
Matthew Denwood [cph] (Original copyright holder of some modified code
where indicated.),
Martyn Plummer [cph] (Original copyright holder of some modified code
where indicated.) |
| Maintainer: |
František Bartoš <f.bartos96 at gmail.com> |
| BugReports: |
https://github.com/FBartos/RoBMA/issues |
| License: |
GPL-3 |
| URL: |
https://fbartos.github.io/RoBMA/ |
| NeedsCompilation: |
yes |
| SystemRequirements: |
JAGS >= 4.3.1 (https://mcmc-jags.sourceforge.io/) |
| Citation: |
RoBMA citation info |
| Materials: |
README NEWS |
| In views: |
Bayesian, MetaAnalysis |
| CRAN checks: |
RoBMA results |
Documentation:
| Reference manual: |
RoBMA.pdf |
| Vignettes: |
Fitting Custom Meta-Analytic Ensembles (source, R code)
Fast Robust Bayesian Meta-Analysis via Spike and Slab Algorithm (source, R code)
Hierarchical Bayesian Model-Averaged Meta-Analysis (source, R code)
Informed Bayesian Model-Averaged Meta-Analysis in Medicine (source, R code)
Informed Bayesian Model-Averaged Meta-Analysis with Binary Outcomes (source, R code)
Robust Bayesian Model-Averaged Meta-Regression (source, R code)
Reproducing Bayesian Model-Averaged Meta-Analysis (source, R code)
Tutorial: Adjusting for Publication Bias in JASP and R - Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis (source, R code)
|
Downloads:
Reverse dependencies:
Linking:
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