.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 |
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 |
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 |
glmnet_wrapper |
Wrapper for fitting a lasso using package 'glmnet'. |
glm_wrapper |
Wrapper for fitting a logistic regression using 'glm'. |
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' |