Package index • mlumr

All functions

add_integration(): Add numerical integration points
calculate_dic(): Calculate DIC for model comparison
calculate_loo(): Calculate LOO-CV for an mlumr_fit
calculate_waic(): Calculate WAIC for an mlumr_fit
check_integration(): Check integration point adequacy
combine_data(): Combine IPD and AgD for unanchored comparison
compare_models(): Compare fitted ML-UMR models
conditional_effects(): Conditional treatment effects
conditional_predict(): Conditional predictions
dbern(): Bernoulli PMF
default_prior_intercept() default_prior_beta() default_prior_sigma(): Default priors used by mlumr()
distr(): Specify a marginal distribution
marginal_effects(): Marginal treatment effects
mlumr(): Fit ML-UMR Model
mlumr_engine(): Get or Set the Stan Engine
naive(): Naive unadjusted indirect comparison
pbern(): Bernoulli CDF
predict(<mlumr_fit>): Predictions from ML-UMR model
prior_cauchy(): Specify a Cauchy prior
prior_exponential(): Specify an exponential prior
prior_normal(): Specify a normal prior
prior_sensitivity(): Prior sensitivity analysis for an ML-UMR fit
prior_student_t(): Specify a Student-t prior
prior_summary(): Summary of priors used by a fitted ML-UMR model
qbern(): Bernoulli quantile function
set_agd(): Set up aggregate data (AgD)
set_ipd(): Set up individual patient data (IPD)
stc(): Simulated treatment comparison via G-computation
unnest_integration(): Expand integration points into a long-format data frame