Robust Inference for Covariate Adjustment in Randomized Clinical Trials

Documentation for package ‘RobinCar’ version 0.3.2

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.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_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