| sparseR-package | sparseR: Implement ranked sparsity for selecting interactions and polynomials |
| cleveland | Data sets |
| coef.sparseR | Predict coefficients or responses for sparseR object |
| datasets | Data sets |
| EBIC | Custom IC functions for stepwise models |
| EBIC.default | Custom IC functions for stepwise models |
| effect_plot | Plot relevant effects of a sparseR object |
| effect_plot.sparseR | Plot relevant effects of a sparseR object |
| effect_plot.sparseRBIC | Plot relevant effects of a sparseR object |
| get_penalties | Helper function to help set up penalties |
| hungarian | Data sets |
| irlcs_radon_syn | Data sets |
| plot.sparseR | Plot relevant properties of sparseR objects |
| predict.sparseR | Predict coefficients or responses for sparseR object |
| print.sparseR | Print sparseR object |
| RAIC | Custom IC functions for stepwise models |
| RBIC | Custom IC functions for stepwise models |
| S | Data sets |
| sparseR | Fit a ranked-sparsity model with regularized regression |
| sparseRBIC_bootstrap | Bootstrap procedure for stepwise regression |
| sparseRBIC_sampsplit | Sample split procedure for stepwise regression |
| sparseRBIC_step | Fit a ranked-sparsity model with forward stepwise RBIC (experimental) |
| sparseR_prep | Preprocess & create a model matrix with interactions + polynomials |
| step_center_to | Centering numeric data to a value besides their mean |
| summary.sparseR | Summary of sparseR model coefficients |
| switzerland | Data sets |
| tidy.step_center_to | Centering numeric data to a value besides their mean |
| va | Data sets |
| Z | Data sets |
| _PACKAGE | sparseR: Implement ranked sparsity for selecting interactions and polynomials |