Constant_cfg            Configuration of a Constant Estimator
Diagnostics_cfg         Configuration of Model Diagnostics
HTE_cfg                 Configuration of Quantities of Interest
KernelSmooth_cfg        Configuration for a Kernel Smoother
Known_cfg               Configuration of Known Model
MCATE_cfg               Configuration of Marginal CATEs
Model_cfg               Base Class of Model Configurations
Model_data              R6 class to represent data to be used in
                        estimating a model
QoI_cfg                 Configuration of Quantities of Interest
SL.glmnet.interaction   Elastic net regression with pairwise
                        interactions
SLEnsemble_cfg          Configuration for a SuperLearner Ensemble
SLLearner_cfg           Configuration of SuperLearner Submodel
Stratified_cfg          Configuration for a Stratification Estimator
VIMP_cfg                Configuration of Variable Importance
add_effect_diagnostic   Add an additional diagnostic to the effect
                        model
add_effect_model        Add an additional model to the joint effect
                        ensemble
add_known_propensity_score
                        Uses a known propensity score
add_moderator           Adds moderators to the configuration
add_outcome_diagnostic
                        Add an additional diagnostic to the outcome
                        model
add_outcome_model       Add an additional model to the outcome ensemble
add_propensity_diagnostic
                        Add an additional diagnostic to the propensity
                        score
add_propensity_score_model
                        Add an additional model to the propensity score
                        ensemble
add_vimp                Adds variable importance information
attach_config           Attach an 'HTE_cfg' to a dataframe
basic_config            Create a basic config for HTE estimation
construct_pseudo_outcomes
                        Construct Pseudo-outcomes
estimate_QoI            Estimate Quantities of Interest
make_splits             Define splits for cross-fitting
predict.SL.glmnet.interaction
                        Prediction for an SL.glmnet object
produce_plugin_estimates
                        Estimate models of nuisance functions
remove_vimp             Removes variable importance information
