robustHD-package {robustHD} | R Documentation |
Robust Methods for High-Dimensional Data
Description
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>).
Details
The DESCRIPTION file:
Package: | robustHD |
Type: | Package |
Title: | Robust Methods for High-Dimensional Data |
Version: | 0.8.1 |
Date: | 2024-06-30 |
Depends: | R (>= 3.5.0), ggplot2 (>= 0.9.2), perry (>= 0.3.0), robustbase (>= 0.9-5) |
Imports: | MASS, Rcpp (>= 0.9.10), grDevices, parallel, stats, utils |
LinkingTo: | Rcpp (>= 0.9.10), RcppArmadillo (>= 0.3.0) |
Suggests: | lars, mvtnorm, testthat |
Description: | 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>). |
License: | GPL (>= 2) |
URL: | https://github.com/aalfons/robustHD |
BugReports: | https://github.com/aalfons/robustHD/issues |
LazyLoad: | yes |
Authors@R: | c(person("Andreas", "Alfons", email = "alfons@ese.eur.nl", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2513-3788")), person("Dirk", "Eddelbuettel", role = "ctb")) |
Author: | Andreas Alfons [aut, cre] (<https://orcid.org/0000-0002-2513-3788>), Dirk Eddelbuettel [ctb] |
Maintainer: | Andreas Alfons <alfons@ese.eur.nl> |
Encoding: | UTF-8 |
RoxygenNote: | 7.3.1 |
Index of help topics:
AIC.seqModel Information criteria for a sequence of regression models TopGear Top Gear car data coef.seqModel Extract coefficients from a sequence of regression models coefPlot Coefficient plot of a sequence of regression models corHuber Robust correlation based on winsorization critPlot Optimality criterion plot of a sequence of regression models diagnosticPlot Diagnostic plots for a sequence of regression models fitted.seqModel Extract fitted values from a sequence of regression models getScale Extract the residual scale of a robust regression model grplars (Robust) groupwise least angle regression lambda0 Penalty parameter for sparse LTS regression nci60 NCI-60 cancer cell panel partialOrder Find partial order of smallest or largest values perry.seqModel Resampling-based prediction error for a sequential regression model plot.seqModel Plot a sequence of regression models predict.seqModel Predict from a sequence of regression models residuals.seqModel Extract residuals from a sequence of regression models rlars Robust least angle regression robustHD-package Robust Methods for High-Dimensional Data rstandard.seqModel Extract standardized residuals from a sequence of regression models setupCoefPlot Set up a coefficient plot of a sequence of regression models setupCritPlot Set up an optimality criterion plot of a sequence of regression models setupDiagnosticPlot Set up a diagnostic plot for a sequence of regression models sparseLTS Sparse least trimmed squares regression standardize Data standardization tsBlocks Construct predictor blocks for time series models tslars (Robust) least angle regression for time series data tslarsP (Robust) least angle regression for time series data with fixed lag length weights.sparseLTS Extract outlier weights from sparse LTS regression models winsorize Data cleaning by winsorization
Author(s)
Andreas Alfons [aut, cre] (<https://orcid.org/0000-0002-2513-3788>), Dirk Eddelbuettel [ctb]
Maintainer: Andreas Alfons <alfons@ese.eur.nl>
References
Alfons (2021) robustHD: An R package for robust regression with high-dimensional data. Journal of Open Source Software, 6(67), 3786. doi:10.21105/joss.03786.
See Also
Useful links: