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]

drtmle_1.1.2.tar.gz
drtmle_1.1.2.zip(r-4.7)drtmle_1.1.2.zip(r-4.6)drtmle_1.1.2.zip(r-4.5)
drtmle_1.1.2.tgz(r-4.6-any)drtmle_1.1.2.tgz(r-4.5-any)
drtmle_1.1.2.tar.gz(r-4.7-any)drtmle_1.1.2.tar.gz(r-4.6-any)
drtmle_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
drtmle/json (API)
NEWS

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

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

On CRAN:

Conda:

causal-inferenceensemble-learningiptwstatistical-inferencetmle

6.94 score 20 stars 1 packages 96 scripts 312 downloads 1 mentions 5 exports 32 dependencies

Last updated from:538a3a264c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK207
source / vignettesOK257
linux-release-x86_64OK189
macos-release-arm64OK172
macos-oldrel-arm64OK191
windows-develOK204
windows-releaseOK167
windows-oldrelOK201
wasm-releaseOK116

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 May 28 2026.

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