Package: drtmle 1.1.2

drtmle: Doubly-Robust Nonparametric Estimation and Inference

Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ van der Laan (2014), <doi:10.1515/ijb-2012-0038>).

Authors:David Benkeser [aut, cre, cph], Nima Hejazi [ctb]

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drtmle.pdf |drtmle.html✨
drtmle/json (API)
NEWS

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

Peer review:

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

On CRAN:

causal-inferenceensemble-learningiptwstatistical-inferencetmle

6.86 score 18 stars 1 packages 89 scripts 308 downloads 1 mentions 5 exports 32 dependencies

Last updated 2 years agofrom:538a3a264c. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-winOKNov 03 2024
R-4.5-linuxOKNov 03 2024
R-4.4-winOKNov 03 2024
R-4.4-macOKNov 03 2024
R-4.3-winOKNov 03 2024
R-4.3-macOKNov 03 2024

Exports:adaptive_iptwcidrtmleSL.npregwald_test

Dependencies:bitopsbootcaToolscodetoolscubaturecvAUCdata.tabledigestforeachfuturefuture.applygamglobalsgplotsgtoolsiteratorsKernSmoothlatticelistenvMASSMatrixMatrixModelsnnlsnpparallellyquadprogquantregRcppROCRSparseMSuperLearnersurvival

drtmle: Doubly-Robust Inference in R

| David Benkeser | Emory University | Department of Biostatistics and Bioinformatics | 1518 Clifton Road, NE | Mailstop: 002-3AA | Atlanta, Georgia, 30322, U.S.A. | [email protected]

Rendered fromusing_drtmle.Rmdusingknitr::rmarkdownon Nov 03 2024.

Last update: 2022-04-20
Started: 2017-08-15

Readme and manuals

Help Manual

Help pageTopics
Compute asymptotically linear IPTW estimators with super learning for the propensity scoreadaptive_iptw
Helper function for averaging lists of estimates generated in the main 'for' loop of 'drtmle'average_est_cov_list
Helper function to average convergence results and drtmle influence function estimates over multiple fitsaverage_ic_list
Compute confidence intervals for drtmle and adaptive_iptw@ci
Confidence intervals for adaptive_iptw objectsci.adaptive_iptw
Confidence intervals for drtmle objectsci.drtmle
TMLE estimate of the average treatment effect with doubly-robust inferencedrtmle
estimateGestimateG
estimateG_loopestimateG_loop
estimategrnestimategrn
estimategrn_loopestimategrn_loop
estimateQestimateQ
estimateQ_loopestimateQ_loop
estimateQrnestimateQrn
estimateQrn_loopestimateQrn_loop
Evaluate usual influence function of IPTWeval_Diptw
Evaluate extra piece of the influence function for the IPTWeval_Diptw_g
Evaluate usual efficient influence functioneval_Dstar
Evaluate extra piece of efficient influence function resulting from misspecification of outcome regressioneval_Dstar_g
Evaluate extra piece of efficient influence function resulting from misspecification of propensity scoreeval_Dstar_Q
Help function to extract models from fitted objectextract_models
fluctuateGfluctuateG
fluctuateQfluctuateQ
fluctuateQ1fluctuateQ1
fluctuateQ2fluctuateQ2
Make list of rows in each validation fold.make_validRows
Helper function to properly format partially cross-validated predictions from a fitted super learner.partial_cv_preds
Plot reduced dimension regression fitsplot.drtmle
Predict method for SL.npregpredict.SL.npreg
Print the output of a '"adaptive_iptw"' object.print.adaptive_iptw
Print the output of ci.adaptive_iptwprint.ci.adaptive_iptw
Print the output of ci.drtmleprint.ci.drtmle
Print the output of a '"drtmle"' object.print.drtmle
Print the output of wald_test.adaptive_iptwprint.wald_test.adaptive_iptw
Print the output of wald_test.drtmleprint.wald_test.drtmle
Helper function to reorder lists according to cvFoldsreorder_list
Super learner wrapper for kernel regressionSL.npreg
Temporary fix for convex combination method mean squared error Relative to existing implementation, we reduce the tolerance at which we declare predictions from a given algorithm the same as anothertmp_method.CC_LS
Temporary fix for convex combination method negative log-likelihood loss Relative to existing implementation, we reduce the tolerance at which we declare predictions from a given algorithm the same as another. Note that because of the way 'SuperLearner' is structure, one needs to install the optimization software separately.tmp_method.CC_nloglik
Wald tests for drtmle and adaptive_iptw objectswald_test
Wald tests for adaptive_iptw objectswald_test.adaptive_iptw
Wald tests for drtmle objectswald_test.drtmle