Robust Bayesian Meta-Analyses
Orthornomal contrast matrix
Prints marginal_summary object for RoBMA method
Prints a fitted RoBMA object
Prints summary object for RoBMA method
Creates a prior distribution
Mean difference contrast matrix
Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data
Check fitted RoBMA object for errors and warnings
Prints summary of "BiBMA.reg" ensemble implied by the specified prio...
Prints summary of "RoBMA" ensemble implied by the specified priors
Prints summary of "RoBMA.reg" ensemble implied by the specified prio...
Combines different effect sizes into a common metric
Independent contrast matrix
Checks a fitted RoBMA object
Effect size transformations
Forest plot for a RoBMA object
Interprets results of a RoBMA model.
Reports whether x is a RoBMA object
Plots marginal estimates of a fitted RoBMA regression object
Summarize marginal estimates of a fitted RoBMA regression object
Estimate a Bayesian Model-Averaged Meta-Analysis
Estimate a Bayesian Model-Averaged Meta-Regression
Plots a fitted RoBMA object
Models plot for a RoBMA object
Creates a prior distribution for factors
Creates an informed prior distribution based on research
Creates a prior distribution
Creates a prior distribution for PET or PEESE models
Creates a prior distribution for PET or PEESE models
Creates a prior distribution for a weight function
RoBMA: Robust Bayesian meta-analysis
Estimate a Robust Bayesian Meta-Analysis
Estimate a Robust Bayesian Meta-Analysis Meta-Regression
Control MCMC fitting process
Options for the RoBMA package
Sample sizes to standard errors calculations
Standard errors transformations
Summarize fitted RoBMA object
Updates a fitted BiBMA object
Updates a fitted RoBMA object
Weighted multivariate normal distribution
Weighted normal distribution
A framework for estimating ensembles of meta-analytic models (assuming either presence or absence of the effect, heterogeneity, and publication bias). 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 non-informative or informative prior distributions for the effect size, heterogeneity, and publication bias components (including selection models and PET-PEESE). The package provides convenient functions for summary, visualizations, and fit diagnostics.