Package: nlpred 1.0.1

nlpred: Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples

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.

Authors:David Benkeser [aut, cre]

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nlpred/json (API)
NEWS

# Install 'nlpred' in R:
install.packages('nlpred', repos = c('https://benkeser.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/benkeser/nlpred/issues

Datasets:

On CRAN:

auccross-validationestimating-equationsmachine-learningtmle

12 exports 3 stars 0.93 score 29 dependencies 6 scripts 214 downloads

Last updated 2 years agofrom:a41d362cd8. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 05 2024
R-4.5-winNOTESep 05 2024
R-4.5-linuxNOTESep 05 2024
R-4.4-winNOTESep 05 2024
R-4.4-macNOTESep 05 2024
R-4.3-winOKSep 05 2024
R-4.3-macOKSep 05 2024

Exports:boot_aucboot_scrnpcv_auccv_scrnpglm_wrapperglmnet_wrapperlpo_aucrandomforest_wrapperranger_wrapperstepglm_wrappersuperlearner_wrapperxgboost_wrapper

Dependencies:assertthatbitopsbootcaToolscodetoolscubaturecvAUCdata.tableforeachgamgplotsgtoolsiteratorsKernSmoothlatticeMASSMatrixMatrixModelsnnlsnpquadprogquantregrbibutilsRcppRdpackROCRSparseMSuperLearnersurvival

nlpred: Small-sample optimized estimators of nonlinear risks

David Benkeser

Rendered fromusing_nlpred.Rmdusingknitr::rmarkdownon Sep 05 2024.

Last update: 2022-06-10
Started: 2019-09-07

Readme and manuals

Help Manual

Help pageTopics
Compute one of the terms of the efficient influence function.Dy
An estimating function for cvAUC.estim_fn
An estimating function for cvAUC with initial estimates generated via nested cross-validation.estim_fn_nested_cv
Compute the AUC given the cdf and pdf of psi.get_auc
Helper function to turn prediction_list into CV estimate of SCRNP.get_cv_estim
Function to estimate density needed to evaluate standard errors..get_density
Helper function to get quantile for a single training fold data when nested CV is used..get_nested_cv_quantile
Helper function to get results for a single cross-validation fold.get_one_fold
Worker function for fitting prediction functions (possibly in parallel).get_predictions
Compute the conditional (given Y = y) estimated distribution of psi.get_psi_distribution
Compute the conditional (given Y = y) CV-estimated distribution of psi.get_psi_distribution_nested_cv
Helper function to get quantile for a single training fold data when nested CV is NOT used..get_quantile
Worker function to make long form data set needed for CVTMLE targeting step.make_long_data
Worker function to make long form data set needed for CVTMLE targeting step when nested cv is used.make_long_data_nested_cv
Helper function for making data set in proper format for CVTMLE.make_targeting_data
Unexported function from cvAUC package.process_input
adultadult
bankbank
Compute the bootstrap-corrected estimator of AUC.boot_auc
Compute the bootstrap-corrected estimator of SCRNP.boot_scrnp
Cardiotocographycardio
ci.cvAUC_withICci.cvAUC_withIC
Estimates of CVAUCcv_auc
Estimates of CV SCNPcv_scrnp
drugsdrugs
Compute the targeted conditional cumulative distribution of the learner at a pointF_nBn_star
Compute the targeted conditional cumulative distribution of the learner at a point where the initial distribution is based on cross validationF_nBn_star_nested_cv
Helper function for CVTMLE grid searchfluc_mod_optim_0
Helper function for CVTMLE grid searchfluc_mod_optim_1
Wrapper for fitting a logistic regression using 'glm'.glm_wrapper
Wrapper for fitting a lasso using package 'glmnet'.glmnet_wrapper
Compute the leave-pair-out cross-validation estimator of AUC.lpo_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_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
Print results of cv_aucprint.cvauc
Print results of cv_scrnpprint.scrnp
Wrapper for fitting a random forest using randomForest.randomforest_wrapper
Wrapper for fitting a random forest using ranger.ranger_wrapper
Wrapper for fitting a forward stepwise logistic regression using 'glm'.stepglm_wrapper
Wrapper for fitting a super learner based on 'SuperLearner'.superlearner_wrapper
winewine
Wrapper for fitting eXtreme gradient boosting via 'xgboost'xgboost_wrapper