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.
Version: | 1.0.3 |
Imports: | runjags, bridgesampling, rjags, coda, psych, stats, graphics, extraDistr, scales, DPQ, callr, Rdpack |
LinkingTo: | BH |
Suggests: | ggplot2, rstan, metaBMA, testthat, vdiffr, knitr, rmarkdown |
Published: | 2020-08-06 |
Author: | František Bartoš |
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 |
Citation: | RoBMA citation info |
Materials: | README NEWS |
CRAN checks: | RoBMA results |
Reference manual: | RoBMA.pdf |
Vignettes: |
Fitting custom meta-analytic ensembles Reproducing BMA Common warnings and errors |
Package source: | RoBMA_1.0.3.tar.gz |
Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
macOS binaries: | r-release: not available, r-oldrel: not available |
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