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enetLTS  

Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression
View on CRAN: Click here


Download and install enetLTS package within the R console
Install from CRAN:
install.packages("enetLTS")

Install from Github:
library("remotes")
install_github("cran/enetLTS")

Install by package version:
library("remotes")
install_version("enetLTS", "1.1.0")



Attach the package and use:
library("enetLTS")
Maintained by
Fatma Sevinc Kurnaz
[Scholar Profile | Author Map]
First Published: 2018-01-22
Latest Update: 2022-05-21
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.
How to cite:
Fatma Sevinc Kurnaz (2018). enetLTS: Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression. R package version 1.1.0, https://cran.r-project.org/web/packages/enetLTS. Accessed 10 Apr. 2025.
Previous versions and publish date:
0.1.0 (2018-01-22 10:31)
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Complete documentation for enetLTS
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