Package: robustHD 0.8.1

robustHD: Robust Methods for High-Dimensional Data

Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression. Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).

Authors:Andreas Alfons [aut, cre], Dirk Eddelbuettel [ctb]

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robustHD.pdf |robustHD.html
robustHD/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/aalfons/robusthd/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

6.66 score 10 stars 8 packages 180 scripts 2.1k downloads 22 exports 33 dependencies

Last updated 4 months agofrom:c033d4276f. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-win-x86_64WARNINGOct 29 2024
R-4.5-linux-x86_64WARNINGOct 29 2024
R-4.4-win-x86_64WARNINGOct 29 2024
R-4.4-mac-x86_64WARNINGOct 29 2024
R-4.4-mac-aarch64WARNINGOct 29 2024
R-4.3-win-x86_64WARNINGOct 29 2024
R-4.3-mac-x86_64WARNINGOct 29 2024
R-4.3-mac-aarch64WARNINGOct 29 2024

Exports:coefPlotcorHubercritPlotdiagnosticPlotgetScalegrplarslambda0partialOrderrgrplarsrlarsrobStandardizertslarsrtslarsPsetupCoefPlotsetupCritPlotsetupDiagnosticPlotsparseLTSstandardizetsBlockstslarstslarsPwinsorize

Dependencies:clicolorspaceDEoptimRfansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmeperrypillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangrobustbasescalestibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Robust Methods for High-Dimensional DatarobustHD-package robustHD
Information criteria for a sequence of regression modelsAIC.seqModel AIC.sparseLTS BIC.seqModel BIC.sparseLTS
Extract coefficients from a sequence of regression modelscoef.grplars coef.perrySeqModel coef.rlars coef.seqModel coef.sparseLTS coef.tslars coef.tslarsP
Coefficient plot of a sequence of regression modelscoefPlot coefPlot.grplars coefPlot.rlars coefPlot.seqModel coefPlot.setupCoefPlot coefPlot.sparseLTS coefPlot.tslars coefPlot.tslarsP
Robust correlation based on winsorizationcorHuber
Optimality criterion plot of a sequence of regression modelscritPlot critPlot.grplars critPlot.perrySeqModel critPlot.perrySparseLTS critPlot.rlars critPlot.seqModel critPlot.setupCritPlot critPlot.sparseLTS critPlot.tslars critPlot.tslarsP
Diagnostic plots for a sequence of regression modelsdiagnosticPlot diagnosticPlot.grplars diagnosticPlot.perrySeqModel diagnosticPlot.perrySparseLTS diagnosticPlot.rlars diagnosticPlot.seqModel diagnosticPlot.setupDiagnosticPlot diagnosticPlot.sparseLTS diagnosticPlot.tslars diagnosticPlot.tslarsP
Extract fitted values from a sequence of regression modelsfitted.grplars fitted.perrySeqModel fitted.rlars fitted.seqModel fitted.sparseLTS fitted.tslars fitted.tslarsP
Extract the residual scale of a robust regression modelgetScale getScale.seqModel getScale.sparseLTS
(Robust) groupwise least angle regressiongrplars grplars.data.frame grplars.default grplars.formula print.grplars rgrplars rgrplars.data.frame rgrplars.default rgrplars.formula
Penalty parameter for sparse LTS regressionlambda0
NCI-60 cancer cell panelcellLineInfo gene geneInfo nci60 protein proteinInfo
Find partial order of smallest or largest valuespartialOrder
Resampling-based prediction error for a sequential regression modelperry.rlars perry.seqModel perry.sparseLTS
Plot a sequence of regression modelsplot.grplars plot.perrySeqModel plot.perrySparseLTS plot.rlars plot.seqModel plot.sparseLTS plot.tslars plot.tslarsP
Predict from a sequence of regression modelspredict.grplars predict.rlars predict.seqModel predict.sparseLTS predict.tslars predict.tslarsP
Extract residuals from a sequence of regression modelsresiduals.grplars residuals.perrySeqModel residuals.rlars residuals.seqModel residuals.sparseLTS residuals.tslars residuals.tslarsP
Robust least angle regressionprint.rlars rlars rlars.default rlars.formula
Extract standardized residuals from a sequence of regression modelsrstandard.grplars rstandard.perrySeqModel rstandard.rlars rstandard.seqModel rstandard.sparseLTS rstandard.tslars rstandard.tslarsP
Set up a coefficient plot of a sequence of regression modelssetupCoefPlot setupCoefPlot.grplars setupCoefPlot.rlars setupCoefPlot.seqModel setupCoefPlot.sparseLTS setupCoefPlot.tslars setupCoefPlot.tslarsP
Set up an optimality criterion plot of a sequence of regression modelssetupCritPlot setupCritPlot.grplars setupCritPlot.perrySeqModel setupCritPlot.perrySparseLTS setupCritPlot.rlars setupCritPlot.seqModel setupCritPlot.sparseLTS setupCritPlot.tslars setupCritPlot.tslarsP
Set up a diagnostic plot for a sequence of regression modelssetupDiagnosticPlot setupDiagnosticPlot.grplars setupDiagnosticPlot.perrySeqModel setupDiagnosticPlot.perrySparseLTS setupDiagnosticPlot.rlars setupDiagnosticPlot.seqModel setupDiagnosticPlot.sparseLTS setupDiagnosticPlot.tslars setupDiagnosticPlot.tslarsP
Sparse least trimmed squares regressionprint.sparseLTS sparseLTS sparseLTS.default sparseLTS.formula
Data standardizationrobStandardize standardize
Top Gear car dataTopGear
Construct predictor blocks for time series modelstsBlocks
(Robust) least angle regression for time series dataprint.tslars rtslars rtslars.default rtslars.formula tslars tslars.default tslars.formula
(Robust) least angle regression for time series data with fixed lag lengthprint.tslarsP rtslarsP rtslarsP.default rtslarsP.formula tslarsP tslarsP.default tslarsP.formula
Extract outlier weights from sparse LTS regression modelsweights.sparseLTS
Data cleaning by winsorizationwinsorize winsorize.data.frame winsorize.default winsorize.matrix