caretSLOPE

<p>This function can be used in a call to <code><a href="/link/caret%3A%3Atrain()?package=SLOPE&version=0.5.1" data-mini-rdoc="SLOPE::caret::train()">caret::train()</a></code> to enable
model tuning using caret. Note that this function does not properly work
with sparse feature matrices and standardization due to the way
resampling is implemented in caret. So for these cases, please
check out <code>trainSLOPE()</code> instead.</p>

Efficient implementations for Sorted L-One Penalized Estimation
(SLOPE): generalized linear models regularized with the sorted L1-norm
(Bogdan et al. 2015). Supported models include
ordinary least-squares regression, binomial regression, multinomial
regression, and Poisson regression. Both dense and sparse  predictor
matrices are supported. In addition, the package features predictor
screening rules that enable fast and efficient solutions to high-dimensional
problems.

Johan Larsson

SLOPE

Sorted L1 Penalized Estimation

Jonas Wallin

Malgorzata Bogdan

Ewout van den Berg

Chiara Sabatti

Emmanuel Candes

Evan Patterson

Weijie Su

Jakub Kała

Krystyna Grzesiak

Michal Burdukiewicz

Jerome Friedman

Trevor Hastie

Rob Tibshirani

Balasubramanian Narasimhan

Noah Simon

Junyang Qian

Akarsh Goyal

caretSLOPE function

<p>This function can be used in a call to <code><a href='https://rdrr.io/pkg/caret/man/train.html'>caret::train()</a></code> to enable
model tuning using caret. Note that this function does not properly work
with sparse feature matrices and standardization due to the way
resampling is implemented in caret. So for these cases, please
check out <code>trainSLOPE()</code> instead.</p>

Model objects for model tuning with caret (deprecated) — caretSLOPE

Model objects for model tuning with caret (deprecated)

caretSLOPE: Model objects for model tuning with caret (deprecated)

Description

Usage

Arguments

Value

See Also