hrqglas: Group Variable Selection for Quantile and Robust Mean Regression

A program that conducts group variable selection for quantile and robust mean regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss. Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check function can be approximated by the Huber loss for the median and the tilted version of Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and has also been shown to be statistical consistent.

Version:	1.1.0
Imports:	Rcpp (≥ 1.0.4), stats, MASS, Matrix, graphics, quantreg
LinkingTo:	Rcpp
Published:	2023-01-30
DOI:	10.32614/CRAN.package.hrqglas
Author:	Shaobo Li [aut, cre], Ben Sherwood [aut]
Maintainer:	Shaobo Li <shaobo.li at ku.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	GitHub: https://github.com/shaobo-li/hrqglas
NeedsCompilation:	yes
Materials:	README
CRAN checks:	hrqglas results

Documentation:

Reference manual:

hrqglas.pdf

Downloads:

Package source:	hrqglas_1.1.0.tar.gz
Windows binaries:	r-devel: hrqglas_1.1.0.zip, r-release: hrqglas_1.1.0.zip, r-oldrel: hrqglas_1.1.0.zip
macOS binaries:	r-release (arm64): hrqglas_1.1.0.tgz, r-oldrel (arm64): hrqglas_1.1.0.tgz, r-release (x86_64): hrqglas_1.1.0.tgz, r-oldrel (x86_64): hrqglas_1.1.0.tgz
Old sources:	hrqglas archive

Reverse dependencies:

Reverse imports:

rqPen

Linking:

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