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Implements an efficient algorithm to fit and tune penalized Support Vector Machine 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: | 2025-02-11 |
DOI: | 10.32614/CRAN.package.hdsvm |
Author: | Yikai Zhang [aut, cre], Qian Tang [aut], Boxiang Wang [aut] |
Maintainer: | Yikai Zhang <yikai-zhang at uiowa.edu> |
License: | GPL-2 |
NeedsCompilation: | yes |
CRAN checks: | hdsvm results |
Reference manual: | hdsvm.pdf |
Vignettes: |
Getting started with hdsvm (source, R code) |
Package source: | hdsvm_1.0.1.tar.gz |
Windows binaries: | r-devel: hdsvm_1.0.1.zip, r-release: hdsvm_1.0.1.zip, r-oldrel: hdsvm_1.0.1.zip |
macOS binaries: | r-devel (arm64): hdsvm_1.0.1.tgz, r-release (arm64): hdsvm_1.0.1.tgz, r-oldrel (arm64): hdsvm_1.0.1.tgz, r-devel (x86_64): hdsvm_1.0.1.tgz, r-release (x86_64): hdsvm_1.0.1.tgz, r-oldrel (x86_64): hdsvm_1.0.1.tgz |
Old sources: | hdsvm 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.