RoBMA

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.

Last 30 days

This package has been downloaded 872 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 31 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 12,541 times in the last 365 days. The downloads are officially high enough to crash an underfunded departmental server. Quite an accomplishment! The day with the most downloads was Jul 21, 2024 with 163 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.