Package: robustHD 0.8.1
Andreas Alfons
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:
robustHD_0.8.1.tar.gz
robustHD_0.8.1.tar.gz(r-4.5-noble)robustHD_0.8.1.tar.gz(r-4.4-noble)
robustHD_0.8.1.tgz(r-4.4-emscripten)robustHD_0.8.1.tgz(r-4.3-emscripten)
robustHD.pdf |robustHD.html✨
robustHD/json (API)
NEWS
| # InstallrobustHD in R: |
| install.packages('robustHD',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/aalfons/robusthd/issues
- TopGear - Top Gear car data
- cellLineInfo - NCI-60 cancer cell panel
- gene - NCI-60 cancer cell panel
- geneInfo - NCI-60 cancer cell panel
- protein - NCI-60 cancer cell panel
- proteinInfo - NCI-60 cancer cell panel
Last updated 5 days agofrom:64d56a785a
Exports:coefPlotcorHubercritPlotdiagnosticPlotgetScalegrplarslambda0partialOrderrgrplarsrlarsrobStandardizertslarsrtslarsPsetupCoefPlotsetupCritPlotsetupDiagnosticPlotsparseLTSstandardizetsBlockstslarstslarsPwinsorize
Dependencies:clicolorspaceDEoptimRfansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmeperrypillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangrobustbasescalestibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Robust Methods for High-Dimensional Data | robustHD-package robustHD |
| Information criteria for a sequence of regression models | AIC.seqModel AIC.sparseLTS BIC.seqModel BIC.sparseLTS |
| Extract coefficients from a sequence of regression models | coef.grplars coef.perrySeqModel coef.rlars coef.seqModel coef.sparseLTS coef.tslars coef.tslarsP |
| Coefficient plot of a sequence of regression models | coefPlot coefPlot.grplars coefPlot.rlars coefPlot.seqModel coefPlot.setupCoefPlot coefPlot.sparseLTS coefPlot.tslars coefPlot.tslarsP |
| Robust correlation based on winsorization | corHuber |
| Optimality criterion plot of a sequence of regression models | critPlot 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 models | diagnosticPlot 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 models | fitted.grplars fitted.perrySeqModel fitted.rlars fitted.seqModel fitted.sparseLTS fitted.tslars fitted.tslarsP |
| Extract the residual scale of a robust regression model | getScale getScale.seqModel getScale.sparseLTS |
| (Robust) groupwise least angle regression | grplars grplars.data.frame grplars.default grplars.formula print.grplars rgrplars rgrplars.data.frame rgrplars.default rgrplars.formula |
| Penalty parameter for sparse LTS regression | lambda0 |
| NCI-60 cancer cell panel | cellLineInfo gene geneInfo nci60 protein proteinInfo |
| Find partial order of smallest or largest values | partialOrder |
| Resampling-based prediction error for a sequential regression model | perry.rlars perry.seqModel perry.sparseLTS |
| Plot a sequence of regression models | plot.grplars plot.perrySeqModel plot.perrySparseLTS plot.rlars plot.seqModel plot.sparseLTS plot.tslars plot.tslarsP |
| Predict from a sequence of regression models | predict.grplars predict.rlars predict.seqModel predict.sparseLTS predict.tslars predict.tslarsP |
| Extract residuals from a sequence of regression models | residuals.grplars residuals.perrySeqModel residuals.rlars residuals.seqModel residuals.sparseLTS residuals.tslars residuals.tslarsP |
| Robust least angle regression | print.rlars rlars rlars.default rlars.formula |
| Extract standardized residuals from a sequence of regression models | rstandard.grplars rstandard.perrySeqModel rstandard.rlars rstandard.seqModel rstandard.sparseLTS rstandard.tslars rstandard.tslarsP |
| Set up a coefficient plot of a sequence of regression models | setupCoefPlot setupCoefPlot.grplars setupCoefPlot.rlars setupCoefPlot.seqModel setupCoefPlot.sparseLTS setupCoefPlot.tslars setupCoefPlot.tslarsP |
| Set up an optimality criterion plot of a sequence of regression models | setupCritPlot 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 models | setupDiagnosticPlot setupDiagnosticPlot.grplars setupDiagnosticPlot.perrySeqModel setupDiagnosticPlot.perrySparseLTS setupDiagnosticPlot.rlars setupDiagnosticPlot.seqModel setupDiagnosticPlot.sparseLTS setupDiagnosticPlot.tslars setupDiagnosticPlot.tslarsP |
| Sparse least trimmed squares regression | print.sparseLTS sparseLTS sparseLTS.default sparseLTS.formula |
| Data standardization | robStandardize standardize |
| Top Gear car data | TopGear |
| Construct predictor blocks for time series models | tsBlocks |
| (Robust) least angle regression for time series data | print.tslars rtslars rtslars.default rtslars.formula tslars tslars.default tslars.formula |
| (Robust) least angle regression for time series data with fixed lag length | print.tslarsP rtslarsP rtslarsP.default rtslarsP.formula tslarsP tslarsP.default tslarsP.formula |
| Extract outlier weights from sparse LTS regression models | weights.sparseLTS |
| Data cleaning by winsorization | winsorize winsorize.data.frame winsorize.default winsorize.matrix |
