metaBMA

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).

Last 30 days

This package has been downloaded 3,070 times in the last 30 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 92 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 33,152 times in the last 365 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The day with the most downloads was Nov 26, 2024 with 223 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.