Package index

Model fitting

Fit the hierarchical Bayesian meta-analysis model.

meta_did()

Fit a Bayesian meta-analysis model across study designs

meta_did_general()

Fit a meta-analysis model with explicit control over design assumptions

set_priors()

Set prior distributions for the meta-analysis model

Prior distributions

Specify prior distributions for model parameters.

normal()

Specify a normal prior

cauchy()

Specify a half-Cauchy prior

gamma()

Specify a gamma prior

Results

Summarise and inspect fitted models.

print(<meta_did_fit>)

Print a meta_did_fit object

summary(<meta_did_fit>)

Summarise a meta_did_fit object

tidy.meta_did_fit()

Tidy a meta_did_fit object

Posterior predictive checks

Diagnose model fit via posterior predictive comparisons.

pp_check_effects()

Posterior predictive check: study-level treatment effects

pp_check_cdf()

Posterior predictive check: CDF comparison

Simulation

Simulate data from the hierarchical DiD model.

simulate_meta_did()

Simulate individual-level DiD data for a collection of studies

Design extractors

Convert simulated individual-level data to the format expected by different study designs, at either the individual or summary level.

as_individual_did()

Extract full individual-level DiD data for use with meta_did()

as_individual_pp()

Extract treatment-arm individual-level pre-post data for use with meta_did()

as_individual_rct()

Extract post-only individual-level RCT data for use with meta_did()

as_summary_did()

Summarise simulated DiD data to study-level statistics

as_summary_did_change()

Summarise simulated DiD data as change-score statistics

as_summary_pp()

Summarise simulated DiD data as pre-post (treatment arm only) statistics

as_summary_rct()

Summarise simulated DiD data as RCT-style post-only statistics

Validation

Validate data inputs before fitting.

validate_summary_data()

Validate summary-level study data

validate_individual_data()

Validate individual-level study data