Function reference • stackBagg

All functions
`algorithms()`	Library of Machine learning procedures
`datagenPaper()`	Simulation settings from Gonzalez Ginestet et al. (2019+). "Stacked IPCW Bagging bagging: a case study in the HIV care registry"
`ipcw_auc()`	IPC Weighted AUC Loss Function
`parametersSimulation()`	Parameter Values for the Simulation
`plot_roc()`	Plot IPCW ROC curve
`prediction_discard()`	Prediction discarding censored observations
`ipcw_ensbagg()` `ipcw_genbagg()` `ipcw_brier()` `ipcw_crossentropy()` `tune_lasso()` `optimun_auc_coef()` `risk_auc()` `MLprocedures()` `ML_list` `MLprocedures_natively()` `ML_list_natively` `grid_parametersDataHIV()`	Algorithm 2: Procedure to obtain optimally the coefficients to be used in Algorithm 1
`stackBagg()`	Stacked IPCW Bagging
`tune_params_ml()`	Tuning parameter selection

Reference