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 |
| Reference manual: | hdqr.html , hdqr.pdf |
| Vignettes: |
Getting started with hdqr (source, R code) |
| Package source: | hdqr_1.0.2.tar.gz |
| Windows binaries: | r-devel: hdqr_1.0.1.zip, r-release: hdqr_1.0.1.zip, r-oldrel: hdqr_1.0.1.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 |
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