R: Robust Inference for Covariate Adjustment in Randomized Clinical Trials

# Robust Inference for Covariate Adjustment in Randomized Clinical Trials

## Documentation for package ‘RobinCar’ version 1.2.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

car_pb | Generate permuted block treatment assignments
car_ps | Generate Pocock-Simon minimization treatment assignments
car_sr | Generate simple randomization treatment assignments
data_gen | Data generation function from JRSS-B paper
data_gen2 | Data generation function from covariate adjusted log-rank paper
print.CalibrationResult | Print calibration result
print.ContrastResult | Print contrast result
print.GLMModelResult | Print glm model result
print.LinModelResult | Print linear model result
print.MHResult | Print MH result
print.TTEResult | Print TTE result
robincar_calibrate | Perform linear or joint calibration
robincar_contrast | Estimate a treatment contrast
robincar_covhr | Covariate-adjusted estimators for time to event data
robincar_coxscore | Robust cox score adjustment
robincar_glm | Covariate adjustment using generalized linear working model
robincar_linear | Covariate adjustment using linear working model
robincar_logrank | Robust (potentially stratified) logrank adjustment
robincar_mh | Estimate Mantel-Haenszel Risk Difference
robincar_SL | BETA: Covariate adjustment using working models from the super learner libraries through the AIPW package with cross-fitting.
robincar_SL_median | BETA: Covariate adjustment using working models from the super learner libraries through the AIPW package with cross-fitting, with median adjustment.
robincar_tte | Covariate adjustment for time to event data