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 |