Man pages for GPLTR
Generalized Partially Linear Tree-Based Regression Model
| bag.aucoob | AUC on the Out Of Bag samples |
| bagging.pltr | bagging pltr models |
| best.tree.BIC.AIC | Prunning the Maximal tree |
| best.tree.bootstrap | parametric bootstrap on a pltr model |
| best.tree.CV | Prunning the Maximal tree |
| best.tree.permute | permutation test on a pltr model |
| burn | burn dataset |
| data_pltr | gpltr data example |
| GPLTR-package | Fit a generalized partially linear tree-based regression... |
| nested.trees | compute the nested trees |
| pltr.glm | Partially tree-based regression model function |
| predict_bagg.pltr | prediction on new features |
| predict_pltr | prediction |
| p.val.tree | Compute the p-value |
| tree2glm | tree to GLM |
| tree2indicators | From a tree to indicators (or dummy variables) |
| VIMPBAG | score of importance for variables |
