# Tidy Estimation of Heterogeneous Treatment Effects

## Documentation for package ‘tidyhte’ version 1.0.4

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

## Help Pages

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
Constant_cfg | Configuration of a Constant Estimator
construct_pseudo_outcomes | Construct Pseudo-outcomes
Diagnostics_cfg | Configuration of Model Diagnostics
estimate_QoI | Estimate Quantities of Interest
HTE_cfg | Configuration of Quantities of Interest
KernelSmooth_cfg | Configuration for a Kernel Smoother
Known_cfg | Configuration of Known Model
make_splits | Define splits for cross-fitting
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
predict.SL.glmnet.interaction | Prediction for an SL.glmnet object
produce_plugin_estimates | Estimate models of nuisance functions
QoI_cfg | Configuration of Quantities of Interest
remove_vimp | Removes variable importance information
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