Fit and Tune Models to Detect Treatment Effect Heterogeneity

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Documentation for package ‘tehtuner’ version 0.3.0

• DESCRIPTION file.
• Package NEWS.

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