Package index • RoBMA

Package

RoBMA-package RoBMA_package RoBMA.package: RoBMA: Robust Bayesian Meta-Analysis
RoBMA.options() RoBMA.get_option(): Options for the RoBMA package
set_autofit_control() set_convergence_checks(): Control MCMC fitting process

Model fitting

brma(): Bayesian Meta-Analysis
brma.glmm(): Bayesian Generalized Meta-Analysis
brma.mv(): Bayesian Meta-Analysis With Known Sampling Covariance
RoBMA(): Robust Bayesian Model-Averaged Meta-Analysis
RoBMA.mv(): Robust Bayesian Model-Averaged Multivariate Meta-Analysis
BMA(): Bayesian Model-Averaged Meta-Analysis
BMA.glmm(): Bayesian Model-Averaged Generalized Meta-Analysis
BMA.mv(): Bayesian Model-Averaged Multivariate Meta-Analysis
bselmodel(): Bayesian Selection Model
bselmodel.mv(): Bayesian Multivariate and Multilevel Selection Model
bPET(): Bayesian Precision-Effect Test (PET) Model
bPET.mv(): Bayesian Multivariate and Multilevel PET Model
bPEESE(): Bayesian Precision-Effect Estimate with Standard Errors (PEESE) Model
bPEESE.mv(): Bayesian Multivariate and Multilevel PEESE Model
update(<brma>): Update a brma Fit

Inputs, priors, and model specification

data_input: Input Data Specification
fitting_specification: Fitting specification
prior_specification: Prior specification
RoBMA_prior_specification: Prior specification for model-averaging
publication_bias_prior_specification bias_prior_specification: Publication-bias prior specification
random_effect_formula_tags: Random-Effect Formula Structure Tags
random_effect_prior_specification random_prior_specification: Random-effect prior specification
prior(): Prior Distribution
prior_none(): Empty Prior
prior_factor(): Factor Prior
prior_ordered(): Ordered Factor Prior
prior_informed(): Informed Prior
prior_PET(): PET Prior
prior_PEESE(): PEESE Prior
prior_weightfunction() wf_cumulative() wf_fixed() wf_independent(): Weightfunction Prior
prior_random() random_block() random_variance_allocation() allocation_ref() random_covariance() prior_lkj() random_monitor() random_new_levels() random_sd_source(): Random-effect prior constructors
selection_model(): Selection Model
known_v_factor(): Declare a diagonal-plus-factor sampling covariance
set_selection_likelihood_control(): Control numerical integration of selection likelihoods
estimate_unit_information_sd(): Estimate Unit Information Standard Deviation
contr.orthonormal() contr.meandif() contr.independent(): BayesTools Contrast Matrices

Summaries and estimates

summary(<brma>) print(<summary.brma>) print(<brma>): Summarize brma Object
interpret() print(<interpret.brma>): Interpret brma Results
summary_models() print(<summary_models.RoBMA>): Summarize Model-Averaged Component Weights
summary_heterogeneity(): Summary of Heterogeneity
summary_heterogeneity(<brma>) print(<summary_heterogeneity.brma_list>): Summary of Heterogeneity for brma Objects
pooled_effect(): Pooled Effect Size
pooled_effect(<brma>): Pooled Effect Size for brma Objects
pooled_heterogeneity(): Pooled Heterogeneity
pooled_heterogeneity(<brma>): Pooled Heterogeneity for brma Objects
marginal_means(): Estimated Marginal Means
marginal_means(<brma>): Estimated Marginal Means for brma Objects
summary(<marginal_means.brma>): Summarize Estimated Marginal Means
true_effects(): True Effects
true_effects(<brma>): True Effects for brma Objects
ranef(): Random Effects
ranef(<brma>): Random Effects for brma Objects
blup(): Best Linear Unbiased Predictions (BLUPs)
blup(<brma>): Best Linear Unbiased Predictions for brma Objects
coef(<brma>): Extract Model Coefficients for brma Objects
fitted(<brma>): Fitted Values for brma Objects
nobs(<brma>): Number of Observations for brma Objects
predict(<brma>): Predict From brma Object
hypothesis() bf_hypothesis() BF_hypothesis(): Hypothesis Bayes Factors
reexports bridge_sampler logml post_prob bf bayes_factor hypothesis_quantities loo_model_weights: Objects exported from other packages

Model comparison

add_marglik(<brma>): Add Marginal Likelihood to brma Objects
bridge_sampler(<brma>): Bridge Sampling for brma Objects
logml(<brma>): Log Marginal Likelihood for brma Objects
post_prob(<brma>): Posterior Model Probabilities for brma Objects
bf(<brma>) bayes_factor(<brma>): Bayes Factor for brma Objects
add_loo(<brma>): Add LOO-PSIS to brma Objects
loo(<brma>): LOO-PSIS for brma Objects
loo_compare(<brma>): Compare brma Models Using LOO
loo_compare(<loo>): Compare loo Objects Using LOO
loo_model_weights(<brma>): Predictive Model Weights for brma Objects
loo_weights(<brma>): Extract Normalized PSIS Weights from brma Object
check_loo(<brma>): Check LOO Diagnostics for brma Objects
add_waic(<brma>): Add WAIC to brma Objects
waic(<brma>): WAIC for brma Objects
log_lik(): Extract Pointwise Log-Likelihood Draws
logLik(<brma>): Extract Log-Likelihood Matrix from brma Object
AIC(<brma>) BIC(<brma>): Unsupported Information Criteria for brma Objects
reexports bridge_sampler logml post_prob bf bayes_factor hypothesis_quantities loo_model_weights: Objects exported from other packages

Diagnostics and influence

influence(<brma>): Measure Influence for brma Objects
cooks.distance(<brma>): Cook's Distance for brma Objects
dfbetas(<brma>): DFBETAS for brma Objects
dffits(<brma>): DFFITS for brma Objects
covratio(<brma>): COVRATIO for brma Objects
hatvalues(<brma>): Hat Values for brma Objects
residuals(<brma>): Residuals for brma Objects
rstandard(<brma>): Internally Standardized Residuals for brma Objects
rstudent(<brma>): Externally Standardized (Studentized) Residuals for brma Objects
vif(): Variance Inflation Factors
vif(<brma>): Variance Inflation Factors for brma Objects
density_diagnostics(): Extract qCMDE/IWMDE Density Diagnostics
selection_sensitivity_diagnostics() add_selection_sensitivity_diagnostics(): Diagnose Selection Conditioning Sensitivity
selection_cache_info(): Inspect or clear the native selection cache
selection_sampler_info(): Inspect corrected selection-sampler counters
remove_selection_cache(): Remove a fitted model's selection cache

Plots

plot(<brma>) lines(<brma>): Plots brma Object
forest(<brma>) forest(<metafor_forest.brma>): Forest Plot for brma Objects Using metafor
as_metafor_forest(): Prepare brma Data for metafor Forest Plots
funnel(<brma>) bfunnel(): Frequentist-Style and Bayesian Funnel Plots for brma Objects
radial(<brma>) galbraith(<brma>): Radial (Galbraith) Plot for brma Object
regplot(<brma>): Regression Plot (Bubble Plot) for brma Object
qqnorm(<brma>): Normal QQ Plot for brma Object
plot_prior(): Plot Prior Distributions
print_prior(): Print Prior Distributions
plot_weightfunction(): Plots Weight Function of brma Object
plot_pet_peese(): Plot PET-PEESE Fit of brma Object
plot_diagnostic() plot_diagnostic_autocorrelation() plot_diagnostic_trace() plot_diagnostic_density(): Plot MCMC Diagnostics
plot(<marginal_means.brma>) lines(<marginal_means.brma>): Plot Estimated Marginal Means

Posterior samples and zplot diagnostics

as_draws() as_draws_array() as_draws_df() as_draws_list() as_draws_matrix() as_draws_rvars(): Convert brma Objects to posterior Draws Formats
as_draws(<brma_samples>) as_draws_array(<brma_samples>) as_draws_df(<brma_samples>) as_draws_list(<brma_samples>) as_draws_matrix(<brma_samples>) as_draws_rvars(<brma_samples>): Convert brma_samples to posterior Draws Formats
as.matrix(<brma_samples>): Convert brma_samples to Matrix
print(<brma_samples>): Print brma_samples Object
summary(<brma_samples>): Summarize brma_samples Object
print(<summary.brma_samples>): Print summary.brma_samples Object
as_zplot(<brma>): Transform brma Object to Zplot
zplot(<brma>) zplot(<zplot_brma>): Plot Zplot Diagnostics Directly
summary(<zplot_brma>): Summarize Zplot Results
print(<summary.zplot_brma>): Print Zplot Summary
plot(<zplot_brma>): Plot Zplot Results
hist(<zplot_brma>): Histogram of Z-Statistics
lines(<zplot_brma>): Add Zplot Density Lines

Example datasets

Anderson2010: 23 experimental studies from Anderson et al. (2010) that meet the best practice criteria
Andrews2021: 39 study rows on household chaos and child executive functions from a meta-analysis by Andrews et al. (2021)
Bem2011: 9 experimental studies from Bem (2011) as described in Bem et al. (2011)
Havrankova2025: 1159 effect sizes from a meta-analysis of beauty and professional success by Havránková et al. (2025)
Hoogeveen2023: 106 analyst-team effect-size estimates of the relation between religiosity and well-being from Hoogeveen et al. (2023)
Hoppen2025: 37 studies from a meta-analysis of social comparison as a behavior change technique by Hoppen et al. (2025)
Johnides2025: 412 effect sizes from a meta-analysis of secondary benefits of family-based treatments by Johnides et al. (2025)
Kroupova2021: 881 estimates from 69 studies of a relationship between employment and educational outcomes collected by Kroupova et al. (2024)
Lui2015: 18 studies of a relationship between acculturation mismatch and intergenerational cultural conflict collected by Lui (2015)
ManyLabs16: 55 effect sizes from Many Labs 2 replication studies of Tversky and Kahneman (1981) framing effects
Poulsen2006: 5 studies with a tactile outcome assessment from Poulsen et al. (2006) of the effect of potassium-containing toothpaste on dentine hypersensitivity
Wang2025: 70 effect sizes from a meta-analysis of ChatGPT's impact on student learning by Wang and Fan (2025)
Weingarten2018: 582 effect sizes examining the ease-of-retrieval effect from a meta-analysis by Weingarten and Hutchinson (2018)

Print helpers

print(<RoBMA_data>): Print method for RoBMA_data objects
print(<marginal_means.brma>): Print Estimated Marginal Means
print(<summary.marginal_means.brma>): Print Summary of Estimated Marginal Means
print(<summary_heterogeneity.brma>): Print Summary of Heterogeneity
print(<vif.brma>): Print VIF Results

Data frame coercion

as.data.frame(<brma>) as.data.frame(<summary.brma>): Convert brma Summaries to a Data Frame
as.data.frame(<brma_samples>) as.data.frame(<summary.brma_samples>) as.data.frame(<brma_samples_list>): Convert brma_samples Results to Data Frames
as.data.frame(<summary_heterogeneity.brma>) as.data.frame(<summary_heterogeneity.brma_list>): Convert Heterogeneity Summaries to Data Frames
as.data.frame(<summary_models.RoBMA>): Convert Model-Weight Summaries to a Data Frame
as.data.frame(<marginal_means.brma>) as.data.frame(<summary.marginal_means.brma>): Convert Marginal Means to a Data Frame
as.data.frame(<zplot_brma>) as.data.frame(<summary.zplot_brma>): Convert Z-Plot Summaries to a Data Frame
as.data.frame(<vif.brma>): Convert VIF Results to a Data Frame
as.data.frame(<infl.brma>): Convert Influence Results to a Data Frame
as.data.frame(<dfbetas.brma>): Convert DFBETAS Results to a Data Frame
as.data.frame(<covratio.brma>): Convert COVRATIO Results to a Data Frame