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; ), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; ), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; ).


Reference manual

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install.packages("robustHD")

0.8.4 by Andreas Alfons, 8 months ago


https://github.com/aalfons/robustHD


Report a bug at https://github.com/aalfons/robustHD/issues


Browse source code at https://github.com/cran/robustHD


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


Documentation:   PDF Manual  


GPL (>= 2) license


Imports MASS, Rcpp, grDevices, parallel, rlang, stats, utils

Depends on ggplot2, perry, robustbase

Suggests lars, mvtnorm, testthat

Linking to Rcpp, RcppArmadillo


Imported by PAMhm, enetLTS, robCompositions, rrcovHD.

Depended on by sparseLTSEigen.

Suggested by ShapleyOutlier, cellWise, classmap.


See at CRAN