enetLTS: Robust and Sparse Methods for High Dimensional Linear and Logistic Regression

Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <doi:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.

Version:	0.1.0
Imports:	ggplot2, glmnet, robustHD, grid, reshape, parallel, cvTools, stats
Published:	2018-01-22
Author:	Fatma Sevinc KURNAZ and Irene HOFFMANN and Peter FILZMOSER
Maintainer:	Fatma Sevinc Kurnaz <fatmasevinckurnaz at gmail.com>
License:	GPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	enetLTS results

Documentation:

Reference manual:

enetLTS.pdf

Downloads:

Package source:	enetLTS_0.1.0.tar.gz
Windows binaries:	r-devel: enetLTS_0.1.0.zip, r-release: enetLTS_0.1.0.zip, r-oldrel: enetLTS_0.1.0.zip
macOS binaries:	r-release (arm64): enetLTS_0.1.0.tgz, r-oldrel (arm64): enetLTS_0.1.0.tgz, r-release (x86_64): enetLTS_0.1.0.tgz, r-oldrel (x86_64): enetLTS_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=enetLTS to link to this page.