Man pages for jackmwolf/tehtuner
Fit and Tune Models to Detect Treatment Effect Heterogeneity
| 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... |
| get_vt2 | Get the appropriate Step 2 estimation function associated... |
| 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 |
| tune_theta | Estimate the penalty parameter for Step 2 of Virtual Twins |
| tunevt | Fit a tuned Virtual Twins model |
| 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... |
| vt1_super | Estimate the CATE Using Super Learner for Step 1 of Virtual... |
| vt2_classtree | Estimate the CATE using a classification tree for Step 2 |
| vt2_ctree | Estimate the CATE using a conditional inference tree for Step... |
| vt2_lasso | Estimate the CATE using the Lasso for Step 2 |
| vt2_rtree | Estimate the CATE using a regression tree for Step 2 |
