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:
enetLTS_1.1.0.tar.gz
enetLTS_1.1.0.zip(r-4.7-any)enetLTS_1.1.0.zip(r-4.6-any)enetLTS_1.1.0.zip(r-4.5-any)
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✨
DESCRIPTION
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
Last updated from:0726cbb903. Checks:7 ERROR, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | ERROR | 137 | ||
| source / vignettes | OK | 159 | ||
| linux-release | ERROR | 130 | ||
| macos-release | ERROR | 154 | ||
| macos-oldrel | ERROR | 143 | ||
| windows-devel | ERROR | 102 | ||
| windows-release | ERROR | 112 | ||
| windows-oldrel | ERROR | 105 | ||
| wasm-release | OK | 96 |
Exports:coef.enetLTScv.enetLTSenetLTSfitted.enetLTSlambda00nonzeroCoef.enetLTSplot.enetLTSplotCoef.enetLTSplotDiagnostic.enetLTSplotResid.enetLTSpredict.enetLTSprint.enetLTSresiduals.enetLTSweights.enetLTS
Dependencies:clicodetoolscpp11cvToolsDEoptimRfarverforeachggplot2glmnetgluegtableisobanditeratorslabelinglatticelifecycleMASSMatrixperryplyrR6RColorBrewerRcppRcppArmadilloRcppEigenreshaperlangrobustbaserobustHDS7scalesshapesurvivalvctrsviridisLitewithr
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 |