Package: enetLTS 1.1.0

enetLTS: Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression

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.

Authors:Fatma Sevinc KURNAZ and Irene HOFFMANN and Peter FILZMOSER

enetLTS_1.1.0.tar.gz
enetLTS_1.1.0.zip(r-4.5)enetLTS_1.1.0.zip(r-4.4)enetLTS_1.1.0.zip(r-4.3)
enetLTS_1.1.0.tgz(r-4.5-any)enetLTS_1.1.0.tgz(r-4.4-any)enetLTS_1.1.0.tgz(r-4.3-any)
enetLTS_1.1.0.tar.gz(r-4.5-noble)enetLTS_1.1.0.tar.gz(r-4.4-noble)
enetLTS_1.1.0.tgz(r-4.4-emscripten)enetLTS_1.1.0.tgz(r-4.3-emscripten)
enetLTS.pdf |enetLTS.html
enetLTS/json (API)

# Install 'enetLTS' in R:
install.packages('enetLTS', repos = c('https://fatmasevinck.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fatmasevinck/enetlts/issues

On CRAN:

Conda:

4.07 score 3 stars 26 scripts 212 downloads 1 mentions 14 exports 44 dependencies

Last updated 2 years agofrom:0726cbb903. Checks:1 OK, 8 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-winERRORMar 10 2025
R-4.5-macERRORMar 10 2025
R-4.5-linuxERRORMar 10 2025
R-4.4-winERRORMar 10 2025
R-4.4-macERRORMar 10 2025
R-4.4-linuxERRORMar 10 2025
R-4.3-winERRORMar 10 2025
R-4.3-macERRORMar 10 2025

Exports:coef.enetLTScv.enetLTSenetLTSfitted.enetLTSlambda00nonzeroCoef.enetLTSplot.enetLTSplotCoef.enetLTSplotDiagnostic.enetLTSplotResid.enetLTSpredict.enetLTSprint.enetLTSresiduals.enetLTSweights.enetLTS

Dependencies:clicodetoolscolorspacecvToolsDEoptimRfansifarverforeachggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmeperrypillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloRcppEigenreshaperlangrobustbaserobustHDscalesshapesurvivaltibbleutf8vctrsviridisLitewithr