R: Doubly-Robust Nonparametric Estimation and Inference

# Doubly-Robust Nonparametric Estimation and Inference

## Documentation for package ‘drtmle’ version 1.1.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

adaptive_iptw | Compute asymptotically linear IPTW estimators with super learning for the propensity score
average_est_cov_list | Helper function for averaging lists of estimates generated in the main 'for' loop of 'drtmle'
average_ic_list | Helper function to average convergence results and drtmle influence function estimates over multiple fits
ci | Compute confidence intervals for drtmle and adaptive_iptw@
ci.adaptive_iptw | Confidence intervals for adaptive_iptw objects
ci.drtmle | Confidence intervals for drtmle objects
drtmle | TMLE estimate of the average treatment effect with doubly-robust inference
estimateG | estimateG
estimategrn | estimategrn
estimategrn_loop | estimategrn_loop
estimateG_loop | estimateG_loop
estimateQ | estimateQ
estimateQrn | estimateQrn
estimateQrn_loop | estimateQrn_loop
estimateQ_loop | estimateQ_loop
eval_Diptw | Evaluate usual influence function of IPTW
eval_Diptw_g | Evaluate extra piece of the influence function for the IPTW
eval_Dstar | Evaluate usual efficient influence function
eval_Dstar_g | Evaluate extra piece of efficient influence function resulting from misspecification of outcome regression
eval_Dstar_Q | Evaluate extra piece of efficient influence function resulting from misspecification of propensity score
extract_models | Help function to extract models from fitted object
fluctuateG | fluctuateG
fluctuateQ | fluctuateQ
fluctuateQ1 | fluctuateQ1
fluctuateQ2 | fluctuateQ2
make_validRows | Make list of rows in each validation fold.
partial_cv_preds | Helper function to properly format partially cross-validated predictions from a fitted super learner.
plot.drtmle | Plot reduced dimension regression fits
predict.SL.npreg | Predict method for SL.npreg
print.adaptive_iptw | Print the output of a '"adaptive_iptw"' object.
print.ci.adaptive_iptw | Print the output of ci.adaptive_iptw
print.ci.drtmle | Print the output of ci.drtmle
print.drtmle | Print the output of a '"drtmle"' object.
print.wald_test.adaptive_iptw | Print the output of wald_test.adaptive_iptw
print.wald_test.drtmle | Print the output of wald_test.drtmle
reorder_list | Helper function to reorder lists according to cvFolds
SL.npreg | Super learner wrapper for kernel regression
tmp_method.CC_LS | 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 another
tmp_method.CC_nloglik | 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.
wald_test | Wald tests for drtmle and adaptive_iptw objects
wald_test.adaptive_iptw | Wald tests for adaptive_iptw objects
wald_test.drtmle | Wald tests for drtmle objects