Reference

Robust estimation and inference for covariate-adaptive randomization schemes. Robust estimation and inference for covariate-adaptive randomization schemes in randomized controlled trials. Includes linear and non-linear (glm) adjustment working models.
robincar_linear()	Covariate adjustment using linear working model
robincar_glm()	Covariate adjustment using generalized linear working model
robincar_calibrate()	Perform linear or joint calibration
robincar_contrast()	Estimate a treatment contrast
robincar_logrank()	Robust (potentially stratified) logrank adjustment
robincar_covhr()	Covariate-adjusted estimators for time to event data
robincar_coxscore()	Robust cox score adjustment
robincar_tte()	Covariate adjustment for time to event data
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.
Summary printing functions.
print(<LinModelResult>)	Print linear model result
print(<GLMModelResult>)	Print glm model result
print(<ContrastResult>)	Print contrast result
print(<CalibrationResult>)	Print calibration result
print(<TTEResult>)	Print TTE result
print(<MHResult>)	Print MH result
Simulation
car_sr()	Generate simple randomization treatment assignments
car_pb()	Generate permuted block treatment assignments
car_ps()	Generate Pocock-Simon minimization treatment assignments
data_gen()	Data generation function from JRSS-B paper
data_gen2()	Data generation function from covariate adjusted log-rank paper