Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials

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Documentation for package ‘sparseR’ version 0.2.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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
_PACKAGE	sparseR: Implement ranked sparsity for selecting interactions and polynomials