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 |
| Reference manual: | enetLTS.pdf |
| 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: enetLTS_0.1.0.tgz, r-oldrel: enetLTS_0.1.0.tgz |
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