Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples
Compute the leave-pair-out cross-validation estimator of AUC.
Internal function used to perform one bootstrap sample. The function `...
Internal function used to perform one bootstrap sample. The function `...
Print results of cv_auc
Print results of cv_scrnp
Compute the bootstrap-corrected estimator of AUC.
Compute the bootstrap-corrected estimator of SCRNP.
ci.cvAUC_withIC
Estimates of CVAUC
Estimates of CV SCNP
Compute one of the terms of the efficient influence function
An estimating function for cvAUC
An estimating function for cvAUC with initial estimates generated via ...
Compute the AUC given the cdf and pdf of psi
Helper function to turn prediction_list into CV estimate of SCRNP
Function to estimate density needed to evaluate standard errors.
Helper function to get quantile for a single training fold data when n...
Helper function to get results for a single cross-validation fold
Worker function for fitting prediction functions (possibly in parallel...
Compute the conditional (given Y = y) estimated distribution of psi
Compute the conditional (given Y = y) CV-estimated distribution of psi
Helper function to get quantile for a single training fold data when n...
Worker function to make long form data set needed for CVTMLE targeting...
Worker function to make long form data set needed for CVTMLE targeting...
Helper function for making data set in proper format for CVTMLE
Unexported function from cvAUC package
Compute the targeted conditional cumulative distribution of the learne...
Compute the targeted conditional cumulative distribution of the learne...
Helper function for CVTMLE grid search
Helper function for CVTMLE grid search
Wrapper for fitting a logistic regression using glm.
Wrapper for fitting a lasso using package glmnet.
Wrapper for fitting a random forest using randomForest .
Wrapper for fitting a random forest using ranger .
Wrapper for fitting a forward stepwise logistic regression using glm...
Wrapper for fitting a super learner based on SuperLearner.
Wrapper for fitting eXtreme gradient boosting via xgboost
Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), <doi:10.1080/01621459.2019.1668794>). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), <doi:10.1214/15-EJS1035>) and other metrics are included.