print.RoBMA

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, Barto<c5><a1> & 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.

Franti<c5><a1>ek Barto<c5><a1>

RoBMA

Robust Bayesian Meta-Analyses

Maximilian Maier

Eric-Jan Wagenmakers

Joris Goosen

print.RoBMA function

Prints a fitted RoBMA object — print.RoBMA

Prints a fitted RoBMA object

print.RoBMA: Prints a fitted RoBMA object

Description

Usage

Arguments

See Also