Package: hrqglas
Type: Package
Title: Group Variable Selection for Quantile and Robust Mean Regression
Version: 1.1.0
Date: 2023-01-29
Authors@R: 
  c(person("Shaobo", "Li", email="shaobo.li@ku.edu", role=c("aut", "cre")),
    person("Ben", "Sherwood", email="ben.sherwood@ku.edu", role=c("aut")))
Maintainer: Shaobo Li <shaobo.li@ku.edu>
Description: 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.
URL: GitHub: https://github.com/shaobo-li/hrqglas
License: GPL (>= 2)
Imports: Rcpp (>= 1.0.4), stats, MASS, Matrix, graphics, quantreg
LinkingTo: Rcpp
RoxygenNote: 7.1.1
Encoding: UTF-8
Repository: https://shaobo-li.r-universe.dev
Date/Publication: 2023-02-28 20:44:03 UTC
RemoteUrl: https://github.com/shaobo-li/hrqglas
RemoteRef: HEAD
RemoteSha: 94994e98e572b2b3dcd59c6b478b819579c1bc14
NeedsCompilation: yes
Packaged: 2026-09-22 08:11:43 UTC; root
Author: Shaobo Li [aut, cre],
  Ben Sherwood [aut]