This vignette overviews selected features of the RoBMA R
package (Bartoš & Maier,
2020). The table is organized by substantive capability and
covers the model families named in its columns.
Green ticks mark available features. Cells marked
limited mean the feature exists but with narrower model
coverage.
| Topic | Feature | brma.norm | brma.mv | brma.glmm | bselmodel | bselmodel.mv | bPET/bPEESE | bPET.mv/bPEESE.mv | BMA.norm | BMA.mv | BMA.glmm | RoBMA | RoBMA.mv |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model / Structure | Fixed- and random-effects models | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Moderation (mods)
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Location-scale models (scale)
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Three-level models (cluster)
|
✓ | No | ✓ | ✓ | No | ✓ | No | ✓ | No | ✓ | ✓ | No | |
Likelihood weights (weights)
|
✓ | No | ✓ | ✓ | No | ✓ | No | ✓ | No | ✓ | ✓ | No | |
| Model averaging | No | No | No | No | No | No | No | ✓ | ✓ | ✓ | ✓ | ✓ | |
| Inclusion Bayes factors | No | No | No | No | No | No | No | ✓ | ✓ | ✓ | ✓ | ✓ | |
Multivariate models (V)
|
No | ✓ | No | No | ✓ | No | ✓ | No | ✓ | No | No | ✓ | |
General random-effects models (random)
|
No | ✓ | No | No | ✓ | No | ✓ | No | ✓ | No | No | ✓ | |
| Robust / sandwich inference | No | No | No | No | No | No | No | No | No | No | No | No | |
Extend MCMC sampling
(update())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
| Priors | Default prior distributions | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Empirical/informed prior distributions
(prior_informed())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
UISD estimation
(estimate_unit_information_sd())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Custom prior distributions
(prior(),
prior_factor(),
prior_ordered())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
| Publication Bias |
Selection models
(prior_weightfunction(),
wf_cumulative(),
wf_fixed(),
wf_independent())
|
No | No | No | ✓ | ✓ | No | No | No | No | No | ✓ | ✓ |
PET / PEESE models
(prior_PET(),
prior_PEESE())
|
No | No | No | No | No | ✓ | ✓ | No | No | No | ✓ | ✓ | |
| Model averaging over bias models | No | No | No | No | No | No | No | No | No | No | ✓ | ✓ | |
| Bias-adjusted summaries / predictions | No | No | No | ✓ | ✓ | ✓ | ✓ | No | No | No | ✓ | ✓ | |
| Trim-fill / fail-safe N | No | No | No | No | No | No | No | No | No | No | No | No | |
| Prediction |
Pooled effect / heterogeneity summaries
(pooled_effect(),
pooled_heterogeneity())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Heterogeneity decomposition
(summary_heterogeneity(),
tau², I², H²)
|
✓ | limited | ✓ | ✓ | limited | ✓ | limited | ✓ | limited | ✓ | ✓ | limited | |
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())
|
✓ | ✓ | limited | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | limited | ✓ | ✓ | |
Bayesian funnel plots
(bfunnel())
|
✓ | ✓ | No | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | No | ✓ | ✓ | |
zplot diagnostics
(as_zplot()/zplot())
|
✓ | ✓ | No | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | No | ✓ | ✓ | |
Weight-function plots
(plot_weightfunction())
|
No | No | No | ✓ | ✓ | No | No | No | No | No | ✓ | ✓ | |
PET-PEESE plots
(plot_pet_peese())
|
No | No | No | No | No | ✓ | ✓ | No | No | No | ✓ | ✓ | |
Radial / Galbraith plots
(radial(),
galbraith())
|
✓ | ✓ | limited | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | limited | ✓ | ✓ | |
Forest plots
(metafor::forest(),
as_metafor_forest())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Baujat plots
(baujat())
|
No | No | No | No | No | No | No | No | No | No | No | No | |
GOSH plots
(gosh())
|
No | No | No | No | No | No | No | No | No | No | No | No | |
L’Abbe plots
(labbe())
|
No | No | No | No | No | No | No | No | No | No | No | No | |
| Residuals / Diagnostics |
Raw residuals
(residuals())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Pearson / standardized residuals
(rstandard())
|
✓ | ✓ | No | No | No | ✓ | ✓ | ✓ | ✓ | No | limited | limited | |
Studentized residuals (LOO-PIT)
(rstudent())
|
✓ | ✓ | limited | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | limited | ✓ | ✓ | |
Q-Q plots
(qqnorm())
|
✓ | ✓ | limited | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | limited | ✓ | ✓ | |
Cook’s distances
(cooks.distance())
|
✓ | ✓ | No | No | No | ✓ | ✓ | ✓ | ✓ | No | limited | limited | |
DFBETAS
(dfbetas())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
DFFITS
(dffits())
|
✓ | ✓ | No | No | No | ✓ | ✓ | ✓ | ✓ | No | limited | limited | |
Covariance ratios
(covratio())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Hat values
(hatvalues())
|
✓ | ✓ | No | No | No | ✓ | ✓ | ✓ | ✓ | No | limited | limited | |
Combined influence summary
(influence())
|
✓ | limited | limited | limited | No | ✓ | limited | ✓ | limited | limited | limited | limited | |
LOO / WAIC diagnostics
(check_loo(),
loo_weights(),
loo::pareto_k_ids(),
loo::pareto_k_table())
|
✓ | limited | limited | ✓ | limited | ✓ | limited | ✓ | limited | limited | ✓ | limited | |
Moderator collinearity diagnostics
(vif())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
| Refit leave-one-out / permutation tests | No | No | No | No | No | 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 | No | No |
WAIC/LOO
(add_waic(),
add_loo(),
waic(),
loo())
|
✓ | limited | limited | ✓ | limited | ✓ | limited | ✓ | limited | limited | ✓ | limited | |
Bayes factors
(bf(),
bayes_factor(),
post_prob())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
WAIC comparison
(loo_compare())
|
✓ | limited | limited | ✓ | limited | ✓ | limited | ✓ | limited | limited | ✓ | limited | |
LOO comparison
(loo_compare(),
loo_model_weights())
|
✓ | limited | limited | ✓ | limited | ✓ | limited | ✓ | limited | limited | ✓ | limited | |
Parameter hypothesis tests
(hypothesis())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
| AIC / BIC | No | No | No | No | No | No | No | No | No | No | No | No | |
| Reporting |
Plain-text interpretation
(interpret())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Sub-model summary
(summary_models())
|
No | No | No | 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
(log_lik(),
logLik())
|
✓ | limited | limited | ✓ | limited | ✓ | limited | ✓ | limited | limited | ✓ | limited | |
Sample size
(nobs())
|
✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Variance-covariance matrix
(vcov())
|
No | No | No | No | No | 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 | No | No | No | No | No | |
Simulated responses
(simulate())
|
No | No | No | No | No | No | No | No | No | No | No | No |