| 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 | 
| RAIC.default | Custom IC functions for stepwise models | 
| RBIC | Custom IC functions for stepwise models | 
| RBIC.default | 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 |