# Feature Coverage

#### František Bartoš

#### 29th of April 2026

This vignette overviews current features of the `RoBMA` R package (Bartoš & Maier, 2020). The table is organized by substantive capability. Some rows point to additional features not (yet) available in the `RoBMA` R package but featured in the `metafor` package (Viechtbauer, 2010) as reference points.

Green ticks mark available features. Cells marked `limited` mean the feature exists but with narrower model coverage.

Topic | Feature | brma.norm | brma.glmm | bselmodel | bPET/bPEESE | BMA.norm | BMA.glmm | RoBMA
--- | --- | --- | --- | --- | --- | --- | --- | ---
Model / Structure | Fixed- and random-effects models | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Moderation (`mods`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Location-scale models (`scale`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Multilevel models (`cluster`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Model averaging | No | No | No | No | ✓ | ✓ | ✓
Inclusion Bayes factors | No | No | No | No | ✓ | ✓ | ✓
General multivariate / covariance structures | No | No | No | No | No | No | No
Robust / sandwich inference | No | No | No | No | No | No | No
Refit / update (`update()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Priors | Default prior distributions | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Empirical/informed prior distributions (`prior_informed()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Custom prior distributions (`prior()`, `prior_factor()`, `prior_weightfunction()`, `prior_none()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
PET / PEESE prior distributions (`prior_PET()`, `prior_PEESE()`) | No | No | No | ✓ | No | No | ✓
Prior-only inspection (`only_priors = TRUE`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Weight-function shape (`wf_cumulative()`, `wf_fixed()`, `wf_independent()`) | No | No | ✓ | No | No | No | ✓
Factor contrasts (`contr.treatment()`, `contr.meandif()`, `contr.orthonormal()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
UISD estimation (`estimate_unit_information_sd()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Publication Bias | Selection models | No | No | ✓ | No | No | No | ✓
PET / PEESE models | No | No | No | ✓ | No | No | ✓
Model averaging over bias models | No | No | No | No | No | No | ✓
Bias-adjusted summaries / predictions | No | No | ✓ | ✓ | No | No | ✓
Trim-fill / fail-safe N | No | No | No | No | No | No | No
Prediction | Pooled effect / heterogeneity summaries (`pooled_effect()`, `pooled_heterogeneity()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Heterogeneity decomposition (`summary_heterogeneity()`, tau², I², H²) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Fitted-value extraction (`fitted()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Prediction for new covariate values (`predict()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Posterior predictive response summaries (`predict()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
True effects / BLUPs / random effects (`true_effects()`, `blup()`, `ranef()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Estimated marginal means (`marginal_means()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Plots | Posterior plots (`plot()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Prior plots (`plot_prior()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Marginal means plots (`marginal_means()` + `plot()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Regression plots (`regplot()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Funnel plots (`funnel()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
zplot diagnostics (`as_zplot()`/`zplot()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Weight-function plots (`plot_weightfunction()`) | No | No | ✓ | No | No | No | ✓
PET-PEESE plots (`plot_pet_peese()`) | No | No | No | ✓ | No | No | ✓
Radial / Galbraith plots (`radial()`, `galbraith()`) | ✓ | limited | ✓ | ✓ | ✓ | limited | ✓
Forest plots (`forest()`) | No | No | No | No | No | No | No
Baujat plots (`baujat()`) | No | No | No | No | No | No | No
GOSH plots (`gosh()`) | No | No | No | No | No | No | No
L’Abbe plots (`labbe()`) | No | No | No | No | No | No | No
Residuals / Diagnostics | Raw residuals (`residuals()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Pearson / standardized residuals (`rstandard()`) | ✓ | No | No | ✓ | ✓ | No | limited
Studentized residuals (LOO-PIT) (`rstudent()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Q-Q plots (`qqnorm()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Cook’s distances (`cooks.distance()`) | ✓ | No | No | ✓ | ✓ | No | No
DFBETAS (`dfbetas()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
DFFITS (`dffits()`) | ✓ | No | No | ✓ | ✓ | No | No
Covariance ratios (`covratio()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Hat values (`hatvalues()`) | ✓ | No | No | ✓ | ✓ | No | No
Combined influence summary (`influence()`) | ✓ | limited | limited | ✓ | ✓ | limited | limited
LOO / WAIC diagnostics (`check_loo()`, `loo::pareto_k_ids()`, `loo::pareto_k_table()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Moderator collinearity diagnostics (`vif()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Refit leave-one-out / permutation tests | No | No | No | No | No | No | No
MCMC Diagnostics | Posterior summaries (Rhat, ESS, MCMC error) (`summary()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Trace plots (`plot_diagnostic_trace()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Density plots (`plot_diagnostic_density()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Autocorrelation plots (`plot_diagnostic_autocorrelation()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Diagnostic-plot wrapper (`plot_diagnostic()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Model Comparison | Marginal likelihood (`add_marglik()`, `logml()`) | ✓ | ✓ | ✓ | ✓ | No | No | No
WAIC/LOO (`add_waic()`, `add_loo()`, `waic()`, `loo()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Bayes factors (`bf()`, `bayes_factor()`, `post_prob()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
WAIC comparison (`loo_compare()`, `loo_weights()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
LOO comparison (`loo_compare()`, `loo_weights()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
AIC / BIC | No | No | No | No | No | No | No
Reporting | Plain-text interpretation (`interpret()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Sub-model summary (`summary_models()`) | No | No | No | No | ✓ | ✓ | ✓
Prior inspection (`print_prior()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Extraction | Posterior draw extraction (`as_draws()`, `as_draws_array()`, `as_draws_df()`, `as_draws_list()`, `as_draws_matrix()`, `as_draws_rvars()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Coefficients (`coef()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Point-wise log-likelihood (`logLik()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Sample size (`nobs()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Variance-covariance matrix (`vcov()`) | No | No | No | No | No | No | No
Credible intervals (`summary()`, `summary_heterogeneity()`, `pooled_effect()`, `pooled_heterogeneity()`) | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Model weights (`weights()`) | No | No | No | No | No | No | No
Simulated responses (`simulate()`) | No | No | No | No | No | No | No

## References

Bartoš, F., & Maier, M. (2020). RoBMA: An R package for robust Bayesian meta-analyses. https://CRAN.R-project.org/package=RoBMA
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03