SL.npreg                Super learner wrapper for kernel regression
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
estimateG_loop          estimateG_loop
estimateQ               estimateQ
estimateQ_loop          estimateQ_loop
estimateQrn             estimateQrn
estimateQrn_loop        estimateQrn_loop
estimategrn             estimategrn
estimategrn_loop        estimategrn_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_Q            Evaluate extra piece of efficient influence
                        function resulting from misspecification of
                        propensity score
eval_Dstar_g            Evaluate extra piece of efficient influence
                        function resulting from misspecification of
                        outcome regression
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
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
