Package: enetLTS
Type: Package
Title: Robust and Sparse Methods for High Dimensional Linear and Binary
        and Multinomial Regression
Version: 1.1.0
Author: Fatma Sevinc KURNAZ and Irene HOFFMANN and Peter FILZMOSER
Maintainer: Fatma Sevinc KURNAZ <fatmasevinckurnaz@gmail.com>
Description: Fully robust versions of the elastic net estimator are
        introduced for linear and binary and multinomial regression, in
        particular high dimensional data. The algorithm searches for
        outlier free subsets on which the classical elastic net
        estimators can be applied. A reweighting step is added to
        improve the statistical efficiency of the proposed estimators.
        Selecting appropriate tuning parameters for elastic net
        penalties are done via cross-validation.
Imports: ggplot2, glmnet, robustHD, grid, reshape, parallel, cvTools,
        stats, robustbase
License: GPL
Encoding: UTF-8
Repository: https://fatmasevinck.r-universe.dev
Date/Publication: 2023-01-17 08:20:32 UTC
RemoteUrl: https://github.com/fatmasevinck/enetlts
RemoteRef: HEAD
RemoteSha: 0726cbb903763b7c33e38e0bc15a48ad1317c51f
NeedsCompilation: no
Packaged: 2026-10-11 15:12:54 UTC; root