Function reference • nlpred

All functions
`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
`.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
`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`

Reference

All functions

Contents