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cff-version: 1.2.0
message: 'To cite package "hrqglas" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'hrqglas: Group Variable Selection for Quantile and Robust Mean Regression'
version: 1.1.0
doi: 10.32614/CRAN.package.hrqglas
abstract: 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.
authors:
- family-names: Li
  given-names: Shaobo
  email: shaobo.li@ku.edu
- family-names: Sherwood
  given-names: Ben
  email: ben.sherwood@ku.edu
repository: https://shaobo-li.r-universe.dev
repository-code: https://github.com/shaobo-li/hrqglas
commit: 94994e98e572b2b3dcd59c6b478b819579c1bc14
url: https://github.com/shaobo-li/hrqglas
date-released: '2023-01-29'
contact:
- family-names: Li
  given-names: Shaobo
  email: shaobo.li@ku.edu