R: Multilevel Unanchored Meta-Regression for Indirect Treatment Comparisons

# Multilevel Unanchored Meta-Regression for Indirect Treatment Comparisons

## Documentation for package ‘mlumr’ version 0.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

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_priors | Default priors used by 'mlumr()'
default_prior_beta | Default priors used by 'mlumr()'
default_prior_intercept | Default priors used by 'mlumr()'
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
prior_summary.default | Summary of priors used by a fitted ML-UMR model
prior_summary.mlumr_fit | 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