The Extensible TMLE Framework


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Documentation for package ‘tmle3’ version 0.2.0

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all_ancestors Helper functions for the NPSEM
bound Bound (Truncate) Likelihoods
CF_Likelihood Counterfactual Likelihood
define_lf Define a Likelihood Factor
define_node A Node (set of variables) in an NPSEM
define_param Define a Parameter
delta_param_ATE PAR = Linear Contrast EY1-EY0
delta_param_OR Odds Ratio odds(Y1)/odds(Y0)
delta_param_PAF PAF = 1 - (1/RR(EY/E0))
delta_param_PAR PAR = Linear Contrast EY-EY0
delta_param_RR Risk Ratio EY1/EY0
density_formula Get and Plot Propensity Scores
discretize_variable Discretize Continuous Variable
ED_from_estimates Get Empirical Mean of EIFs from Estimates
fit_tmle3 TMLE fit object
get_propensity_scores Get and Plot Propensity Scores
LF_base Base Class for Defining Likelihood Factors
LF_derived Derived Likelihood Factor Estimated from Data + Other Likelihood values, using sl3.
LF_emp Likelihood Factor Estimated using Empirical Distribution
LF_fit Likelihood Factor Estimated from Data using sl3.
LF_known Known True Likelihood Factor
LF_static Static Likelihood Factor
LF_targeted Use a likelihood factor from an existing targeted likelihood
Likelihood Class for Likelihood
Likelihood_cache Cache Likelihood values, update those values
make_CF_Likelihood Counterfactual Likelihood
make_Likelihood Class for Likelihood
make_tmle3_Task Class for Storing Data and NPSEM for TMLE
Param_ATC Additive Effect of Treatment Among the Treated
Param_ATE Average Treatment Effect
Param_ATT Additive Effect of Treatment Among the Treated
Param_base Base Class for Defining Parameters
Param_delta Delta Method Parameters
Param_mean Mean of Outcome Node
Param_MSM Stratified Parameter Estimates via MSM
Param_stratified Stratified Parameter Estimates
Param_survival Survival Curve
Param_TSM Treatment Specific Mean
plot_vim Plot results of variable importance analysis
point_tx_likelihood Helper Functions for Point Treatment
point_tx_npsem Helper Functions for Point Treatment
point_tx_task Helper Functions for Point Treatment
process_missing Preprocess Data to Handle Missing Variables
propensity_score_plot Get and Plot Propensity Scores
propensity_score_table Get and Plot Propensity Scores
submodel_logit Logistic Submodel Fluctuation
summary_from_estimates Summarize Estimates
survival_tx_likelihood Helper Functions for Survival Analysis
survival_tx_npsem Helper Functions for Survival Analysis
survival_tx_task Helper Functions for Survival Analysis
Targeted_Likelihood Targeted Likelihood
time_ordering Helper functions for the NPSEM
tmle3 TMLE from a tmle3_Spec object
tmle3_Fit TMLE fit object
tmle3_Node A Node (set of variables) in an NPSEM
tmle3_Spec Defines a TML Estimator (except for the data)
tmle3_Spec_ATC Defines a TML Estimator (except for the data)
tmle3_Spec_ATE Defines a TML Estimator (except for the data)
tmle3_Spec_ATT Defines a TML Estimator (except for the data)
tmle3_Spec_MSM Defines a Stratified TML Estimator with MSM (except for the data)
tmle3_Spec_OR Defines a TML Estimator for the Odds Ratio
tmle3_Spec_PAR Defines a tmle (minus the data)
tmle3_Spec_RR Defines a TML Estimator for the Risk Ratio
tmle3_Spec_stratified Defines a Stratified TML Estimator (except for the data)
tmle3_Spec_survival Defines a TML Estimator (except for the data)
tmle3_Spec_TSM_all Defines a TML Estimator (except for the data)
tmle3_Task Class for Storing Data and NPSEM for TMLE
tmle3_Update Defines an update procedure (submodel+loss function)
tmle3_Update_survival Defines an update procedure (submodel+loss function) for survival data
tmle3_vim Compute Variable Importance Measures (VIM) with any given parameter
tmle_ATC All Treatment Specific Means
tmle_ATE All Treatment Specific Means
tmle_ATT All Treatment Specific Means
tmle_MSM Make MSM version of Stratified TML estimator class
tmle_OR Odds Ratio
tmle_PAR PAR and PAF
tmle_RR Risk Ratio
tmle_stratified Stratified version of TML estimator from other Spec classes
tmle_survival Treatment Specific Survival
tmle_TSM_all All Treatment Specific Means
train_lf Manually Train Likelihood Factor The internal training process for likelihood factors is somewhat obtuse, so this function does the steps to manually train one, which is helpful if you want to use a likelihood factor independently of a likelihood object