hdqr: Fast Algorithm for Penalized Quantile Regression

Implements an efficient algorithm for fitting the entire regularization path of quantile regression models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.

Version:	1.0.2
Depends:	R (≥ 3.5.0)
Imports:	stats, Matrix, methods
Suggests:	knitr, rmarkdown
Published:	2025-09-26
DOI:	10.32614/CRAN.package.hdqr
Author:	Qian Tang [aut, cre], Yikai Zhang [aut], Boxiang Wang [aut]
Maintainer:	Qian Tang <qian-tang at uiowa.edu>
License:	GPL-2
NeedsCompilation:	yes
Citation:	hdqr citation info
CRAN checks:	hdqr results

Documentation:

Reference manual:	hdqr.html , hdqr.pdf
Vignettes:	Getting started with hdqr (source, R code)

Downloads:

Package source:	hdqr_1.0.2.tar.gz
Windows binaries:	r-devel: hdqr_1.0.2.zip, r-release: hdqr_1.0.2.zip, r-oldrel: hdqr_1.0.2.zip
macOS binaries:	r-release (arm64): hdqr_1.0.2.tgz, r-oldrel (arm64): hdqr_1.0.2.tgz, r-release (x86_64): hdqr_1.0.2.tgz, r-oldrel (x86_64): hdqr_1.0.2.tgz
Old sources:	hdqr archive

Reverse dependencies:

Reverse imports:

QuanDA

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