Package index • RoBMA

All functions

adjusted_effect(): Compute adjusted effect size

Anderson2010: 27 experimental studies from Anderson et al. (2010) that meet the best practice criteria

Andrews2021: 36 estimates of the effect of household chaos on child executive functions with the mean age and assessment type covariates from a meta-analysis by Andrews et al. (2021)

as_zcurve(): Transform RoBMA object into z-curve

Bem2011: 9 experimental studies from Bem (2011) as described in Bem et al. (2011)

BiBMA(): Estimate a Bayesian Model-Averaged Meta-Analysis of Binomial Data

BiBMA.reg(): Estimate a Robust Bayesian Meta-Analysis Meta-Regression

check_RoBMA() check_RoBMA_convergence(): Check fitted RoBMA object for errors and warnings

check_setup.BiBMA(): Prints summary of "BiBMA.reg" ensemble implied by the specified priors and formula

check_setup() check_setup.RoBMA(): Prints summary of "RoBMA" ensemble implied by the specified priors

check_setup.reg() check_setup.RoBMA.reg(): Prints summary of "RoBMA.reg" ensemble implied by the specified priors and formula

combine_data(): Combines different effect sizes into a common metric

contr.orthonormal() contr.meandif() contr.independent(): BayesTools Contrast Matrices

diagnostics() diagnostics_autocorrelation() diagnostics_trace() diagnostics_density(): Checks a fitted RoBMA object

d2r() d2z() d2logOR() d2OR() r2d() r2z() r2logOR() r2OR() z2r() z2d() z2logOR() z2OR() logOR2r() logOR2z() logOR2d() logOR2OR() OR2r() OR2z() OR2logOR() OR2d(): Effect size transformations

extract_posterior(): Extract Posterior Samples from a RoBMA Model

forest(): Forest plot for a RoBMA object

funnel(): Funnel plot for a RoBMA object

hist(<zcurve_RoBMA>): Create Histogram of Z-Statistics

Hoppen2025: 37 studies from a meta-analysis of social comparison as a behavior change technique by Hoppen et al. (2025)

interpret(): Interprets results of a RoBMA model.

is.RoBMA() is.RoBMA.reg() is.NoBMA() is.NoBMA.reg() is.BiBMA() is.BiBMA.reg(): Reports whether x is a RoBMA object

Kroupova2021: 881 estimates from 69 studies of a relationship between employment and educational outcomes collected by Kroupova et al. (2021)

lines(<zcurve_RoBMA>): Add Lines With Posterior Predictive Distribution of Z-Statistics

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

marginal_plot(): Plots marginal estimates of a fitted RoBMA regression 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(<RoBMA>): Plots a fitted RoBMA object

plot(<zcurve_RoBMA>): Create Z-Curve Meta-Analytic Plot

plot_models(): Models plot for a RoBMA object

pooled_effect(): Compute pooled effect size

Poulsen2006: 5 studies with a tactile outcome assessment from Poulsen et al. (2006) of the effect of potassium-containing toothpaste on dentine hypersensitivity

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(): Creates a prior distribution

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-package 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

set_autofit_control() set_convergence_checks(): Control MCMC fitting process

RoBMA.options() RoBMA.get_option(): Options for the RoBMA package

se_d() n_d() se_r() n_r() se_z() n_z(): 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

se_d2se_logOR() se_d2se_r() se_r2se_d() se_logOR2se_d() se_d2se_z() se_r2se_z() se_r2se_logOR() se_logOR2se_r() se_logOR2se_z() se_z2se_d() se_z2se_r() se_z2se_logOR(): Standard errors transformations

summary(<RoBMA>): Summarize fitted RoBMA object

summary(<zcurve_RoBMA>): Summarize fitted zcurve_RoBMA object

summary_heterogeneity(): Summarizes heterogeneity of a RoBMA model

true_effects(): Compute estimated true effect sizes

update(<BiBMA>): Updates a fitted BiBMA object

update(<RoBMA>): Updates a fitted RoBMA object

Wang2025: 70 effect sizes from a meta-analysis of ChatGPT's impact on student learning by Wang and Fan (2025)

weighted_multivariate_normal: Weighted multivariate normal distribution

dwnorm() pwnorm() qwnorm() rwnorm(): Weighted normal distribution

Weingarten2018: 582 effect sizes examining the ease-of-retrieval effect from a meta-analysis by Weingarten and Hutchinson (2018)