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-4.7)enetLTS_1.1.0.zip(r-4.6)enetLTS_1.1.0.zip(r-4.5)
enetLTS_1.1.0.tgz(r-4.6-any)enetLTS_1.1.0.tgz(r-4.5-any)
enetLTS_1.1.0.tar.gz(r-4.7-any)enetLTS_1.1.0.tar.gz(r-4.6-any)
enetLTS_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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:

4.24 score 3 stars 26 scripts 1.5k downloads 1 mentions 14 exports 36 dependencies

Last updated from:0726cbb903. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	145
source / vignettes	OK	145
linux-release-x86_64	ERROR	135
macos-release-arm64	ERROR	141
macos-oldrel-arm64	ERROR	140
windows-devel	ERROR	110
windows-release	ERROR	103
windows-oldrel	ERROR	109
wasm-release	OK	99

Exports:coef.enetLTS cv.enetLTS enetLTS fitted.enetLTS lambda00 nonzeroCoef.enetLTS plot.enetLTS plotCoef.enetLTS plotDiagnostic.enetLTS plotResid.enetLTS predict.enetLTS print.enetLTS residuals.enetLTS weights.enetLTS

Dependencies:cli codetools cpp11 cvTools DEoptimR farver foreach ggplot2 glmnet glue gtable isoband iterators labeling lattice lifecycle MASS Matrix perry plyr R6 RColorBrewer Rcpp RcppArmadillo RcppEigen reshape rlang robustbase robustHD S7 scales shape survival vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
coefficients from the 'enetLTS' object	coef.enetLTS
Cross-validation for the 'enetLTS' object	cv.enetLTS
Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression	enetLTS
the fitted values from the '"enetLTS"' object.	fitted.enetLTS
Upper limit of the penalty parameter for 'family="binomial"'	lambda00
nonzero coefficients indices from the '"enetLTS"' object	nonzeroCoef.enetLTS
plots from the '"enetLTS"' object	plot.enetLTS
coefficients plots from the '"enetLTS"' object	plotCoef.enetLTS
diagnostics plots from the '"enetLTS"' object	plotDiagnostic.enetLTS
residuals plots from the '"enetLTS"' object	plotResid.enetLTS
make predictions from the '"enetLTS"' object.	predict.enetLTS
print from the '"enetLTS"' object	print.enetLTS
the residuals from the '"enetLTS"' object	residuals.enetLTS
binary weights from the '"enetLTS"' object	weights.enetLTS