R: Fit and Tune Models to Detect Treatment Effect Heterogeneity

# Fit and Tune Models to Detect Treatment Effect Heterogeneity

## Documentation for package ‘tehtuner’ version 0.3.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

get_mnpp | Get the MNPP for the Step 2 model
get_mnpp.classtree | Get the MNPP for a Classification Tree
get_mnpp.ctree | Get the MNPP for a Conditional Inference Tree
get_mnpp.lasso | Get the MNPP for a Model fit via Lasso
get_mnpp.rtree | Get the MNPP for a Regression Tree
get_theta_null | Permute a dataset under the null hypothesis and get the MNPP
get_vt1 | Get the appropriate Step 1 estimation function associated with a method
get_vt2 | Get the appropriate Step 2 estimation function associated with a method
permute | Generate a dataset with permuted treatment indicators
print.tunevt | Print an object of class tunevt
tehtuner_example | Simulated example data
test_null_theta_ctree | Test if a Value Gives a Null Conditional Inference Tree
tunevt | Fit a tuned Virtual Twins model
tune_theta | Estimate the penalty parameter for Step 2 of Virtual Twins
validate_alpha0 | Check if alpha0 is a valid input to tunevt
validate_p_reps | Check if p_reps is a valid input to tunevt
validate_Trt | Check if Trt is a valid input to tunevt
validate_Y | Check if Y is a valid input to tunevt
vt1_lasso | Estimate the CATE Using the Lasso for Step 1 of Virtual Twins
vt1_mars | Estimate the CATE Using MARS for Step 1 of Virtual Twins
vt1_rf | Estimate the CATE Using a Random Forest for Step 1 of Virtual Twins
vt1_super | Estimate the CATE Using Super Learner for Step 1 of Virtual Twins
vt2_classtree | Estimate the CATE using a classification tree for Step 2
vt2_ctree | Estimate the CATE using a conditional inference tree for Step 2
vt2_lasso | Estimate the CATE using the Lasso for Step 2
vt2_rtree | Estimate the CATE using a regression tree for Step 2