RoBMA3.6.0 package

Robust Bayesian Meta-Analyses

extract_posterior

Extract Posterior Samples from a RoBMA Model

forest

Forest plot for a RoBMA object

funnel

Funnel plot for a RoBMA object

marginal_summary

Summarize marginal estimates of a fitted RoBMA regression object

NoBMA

Estimate a Bayesian Model-Averaged Meta-Analysis

NoBMA.reg

Estimate a Bayesian Model-Averaged Meta-Regression

plot_models

Models plot for a RoBMA object

plot.RoBMA

Plots a fitted RoBMA object

prior

Creates a prior distribution

BiBMA.reg

Estimate a Robust Bayesian Meta-Analysis Meta-Regression

check_RoBMA

Check fitted RoBMA object for errors and warnings

adjusted_effect

Compute adjusted effect size

as_zcurve

Transform RoBMA object into z-curve

BiBMA

Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data

check_setup.BiBMA

Prints summary of "BiBMA.reg" ensemble implied by the specified prio...

check_setup

Prints summary of "RoBMA" ensemble implied by the specified priors

check_setup.reg

Prints summary of "RoBMA.reg" ensemble implied by the specified prio...

combine_data

Combines different effect sizes into a common metric

contr.BayesTools

BayesTools Contrast Matrices

diagnostics

Checks a fitted RoBMA object

effect_sizes

Effect size transformations

hist.zcurve_RoBMA

Create Histogram of Z-Statistics

interpret

Interprets results of a RoBMA model.

is.RoBMA

Reports whether x is a RoBMA object

lines.zcurve_RoBMA

Add Lines With Posterior Predictive Distribution of Z-Statistics

marginal_plot

Plots marginal estimates of a fitted RoBMA regression object

plot.zcurve_RoBMA

Create Z-Curve Meta-Analytic Plot

pooled_effect

Compute pooled effect size

predict.RoBMA

Predict method for Robust Bayesian Meta-Analysis Fits

print.marginal_summary.RoBMA

Prints marginal_summary object for RoBMA method

print.RoBMA

Prints a fitted RoBMA object

print.summary.RoBMA

Prints summary object for RoBMA method

print.summary.zcurve_RoBMA

Prints summary object for zcurve_RoBMA method

print.zcurve_RoBMA

Prints a fitted zcurve_RoBMA object

prior_factor

Creates a prior distribution for factors

prior_informed

Creates an informed prior distribution based on research

prior_none

Creates a prior distribution

prior_PEESE

Creates a prior distribution for PET or PEESE models

prior_PET

Creates a prior distribution for PET or PEESE models

prior_weightfunction

Creates a prior distribution for a weight function

residuals.RoBMA

Extract method for Robust Bayesian Meta-Analysis Fits

RoBMA_control

Control MCMC fitting process

RoBMA_options

Options for the RoBMA package

RoBMA-package

RoBMA: Robust Bayesian meta-analysis

RoBMA

Estimate a Robust Bayesian Meta-Analysis

RoBMA.reg

Estimate a Robust Bayesian Meta-Analysis Meta-Regression

sample_sizes

Sample sizes to standard errors calculations

set_default_binomial_priors

Set default prior distributions for binomial meta-analytic models

set_default_priors

Set default prior distributions

standard_errors

Standard errors transformations

summary_heterogeneity

Summarizes heterogeneity of a RoBMA model

summary.RoBMA

Summarize fitted RoBMA object

summary.zcurve_RoBMA

Summarize fitted zcurve_RoBMA object

true_effects

Compute estimated true effect sizes

update.BiBMA

Updates a fitted BiBMA object

update.RoBMA

Updates a fitted RoBMA object

weighted_multivariate_normal

Weighted multivariate normal distribution

weighted_normal

Weighted normal distribution

A framework for estimating ensembles of meta-analytic, meta-regression, and multilevel 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>; Bartoš et al., 2025, <doi:10.1037/met0000737>). 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.

  • Maintainer: František Bartoš
  • License: GPL-3
  • Last published: 2025-09-10