RoBMA: Robust Bayesian Meta-Analysis Framework

RoBMA is a framework for estimating ensembles of meta-analytic, meta-regression, and multilevel models. It uses Bayesian model-averaging to combine competing models, weights posterior parameter distributions based on posterior model probabilities, and uses Bayes factors to test for the presence or absence of components like an effect or heterogeneity. The package, authored by František Bartoš, provides functions for summary, visualizations, and fit diagnostics.

Use Cases

• Conducting a meta-analysis that tests for the presence of an effect based on the Bayesian model-averaging framework.
• Assessing publication bias in research synthesis using selection models and PET-PEESE methods described in the framework.
• Performing meta-regression to investigate the influence of moderators on effect sizes.
• Comparing model fit and diagnostics for different prior distributions on effect size and heterogeneity.

Strengths

• Framework is supported by multiple peer-reviewed publications (e.g., Bartoš et al., 2022, 2025).
• Allows users to define a wide range of prior distributions for effect size, heterogeneity, and publication bias components.
• Provides convenient functions for summary, visualizations, and fit diagnostics as stated in the description.

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Likely a software package and methodological framework for statistical analysis.