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,

- Updated citation for CRAN
- Added examples for meta_bma() and meta_random()
- Minor bug fixes

- Data sets 'power_pose' and 'power_pose_unfamiliar' added
- Data set 'facial_feedback' added
- More informative description file
- Requirements for CRAN

- First stable version
- High-level functions meta_bma() and meta_default() perform model averaging for standard models (fixed, random + H0, H1)
- Plotting functions for averaged/random-effects/fixed-effects meta-analysis via plot_forest() and plot_posterior()
- Meta-analysis models are fitted by meta_fixed() and meta_random()
- Effect estimates of fitted meta-analysis models can be averaged by bma()
- Inclusion Bayes factor are computed by inclusion()
- User-specified and default prior functions are specified via prior() [can be plottet via plot(prior)]