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Implements an efficient algorithm to fit and tune penalized quantile regression models using the generalized coordinate descent algorithm. Designed to handle high-dimensional datasets effectively, with emphasis on precision and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, Matrix, methods |
| Suggests: | knitr, rmarkdown |
| Published: | 2025-02-12 |
| 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 |
| CRAN checks: | hdqr results |
| Reference manual: | hdqr.pdf |
| Vignettes: |
Getting started with hdqr (source, R code) |
| Package source: | hdqr_1.0.1.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-devel (arm64): hdqr_1.0.1.tgz, r-release (arm64): hdqr_1.0.1.tgz, r-oldrel (arm64): hdqr_1.0.1.tgz, r-devel (x86_64): hdqr_1.0.1.tgz, r-release (x86_64): hdqr_1.0.1.tgz, r-oldrel (x86_64): hdqr_1.0.1.tgz |
| Old sources: | hdqr archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.