R: Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials

# Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials

## Documentation for package ‘sparseR’ version 0.3.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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