.Dy                     Compute one of the terms of the efficient
                        influence function
.estim_fn               An estimating function for cvAUC
.estim_fn_nested_cv     An estimating function for cvAUC with initial
                        estimates generated via nested cross-validation
.get_auc                Compute the AUC given the cdf and pdf of psi
.get_cv_estim           Helper function to turn prediction_list into CV
                        estimate of SCRNP
.get_density            Function to estimate density needed to evaluate
                        standard errors.
.get_nested_cv_quantile
                        Helper function to get quantile for a single
                        training fold data when nested CV is used.
.get_one_fold           Helper function to get results for a single
                        cross-validation fold
.get_predictions        Worker function for fitting prediction
                        functions (possibly in parallel)
.get_psi_distribution   Compute the conditional (given Y = y) estimated
                        distribution of psi
.get_psi_distribution_nested_cv
                        Compute the conditional (given Y = y)
                        CV-estimated distribution of psi
.get_quantile           Helper function to get quantile for a single
                        training fold data when nested CV is NOT used.
.make_long_data         Worker function to make long form data set
                        needed for CVTMLE targeting step
.make_long_data_nested_cv
                        Worker function to make long form data set
                        needed for CVTMLE targeting step when nested cv
                        is used
.make_targeting_data    Helper function for making data set in proper
                        format for CVTMLE
.process_input          Unexported function from cvAUC package
F_nBn_star              Compute the targeted conditional cumulative
                        distribution of the learner at a point
F_nBn_star_nested_cv    Compute the targeted conditional cumulative
                        distribution of the learner at a point where
                        the initial distribution is based on cross
                        validation
adult                   adult
bank                    bank
boot_auc                Compute the bootstrap-corrected estimator of
                        AUC.
boot_scrnp              Compute the bootstrap-corrected estimator of
                        SCRNP.
cardio                  Cardiotocography
ci.cvAUC_withIC         ci.cvAUC_withIC
cv_auc                  Estimates of CVAUC
cv_scrnp                Estimates of CV SCNP
drugs                   drugs
fluc_mod_optim_0        Helper function for CVTMLE grid search
fluc_mod_optim_1        Helper function for CVTMLE grid search
glm_wrapper             Wrapper for fitting a logistic regression using
                        'glm'.
glmnet_wrapper          Wrapper for fitting a lasso using package
                        'glmnet'.
lpo_auc                 Compute the leave-pair-out cross-validation
                        estimator of AUC.
one_boot_auc            Internal function used to perform one bootstrap
                        sample. The function 'try's to fit 'learner' on
                        a bootstrap sample. If for some reason (e.g.,
                        the bootstrap sample contains no observations
                        with 'Y = 1') the learner fails, then the
                        function returns 'NA'. These 'NA's are ignored
                        later when computing the bootstrap corrected
                        estimate.
one_boot_scrnp          Internal function used to perform one bootstrap
                        sample. The function 'try's to fit 'learner' on
                        a bootstrap sample. If for some reason (e.g.,
                        the bootstrap sample contains no observations
                        with 'Y = 1') the learner fails, then the
                        function returns 'NA'. These 'NA's are ignored
                        later when computing the bootstrap corrected
                        estimate.
print.cvauc             Print results of cv_auc
print.scrnp             Print results of cv_scrnp
randomforest_wrapper    Wrapper for fitting a random forest using
                        randomForest.
ranger_wrapper          Wrapper for fitting a random forest using
                        ranger.
stepglm_wrapper         Wrapper for fitting a forward stepwise logistic
                        regression using 'glm'.
superlearner_wrapper    Wrapper for fitting a super learner based on
                        'SuperLearner'.
wine                    wine
xgboost_wrapper         Wrapper for fitting eXtreme gradient boosting
                        via 'xgboost'
