Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.
| Version: | 0.6.0 |
| Imports: | stats, utils,ggplot2 (≥ 2.2.0),pracma,EbayesThresh |
| Published: | 2022-03-16 |
| DOI: | 10.32614/CRAN.package.POCRE |
| Author: | Dabao Zhang, Zhongli Jiang, Zeyu Zhang, Yu-ting Chen |
| Maintainer: | Dabao Zhang <zhangdb at purdue.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | POCRE results |
| Reference manual: | POCRE.html ,POCRE.pdf |
| Package source: | POCRE_0.6.0.tar.gz |
| Windows binaries: | r-devel:POCRE_0.6.0.zip, r-release:POCRE_0.6.0.zip, r-oldrel:POCRE_0.6.0.zip |
| macOS binaries: | r-release (arm64):POCRE_0.6.0.tgz, r-oldrel (arm64):POCRE_0.6.0.tgz, r-release (x86_64):POCRE_0.6.0.tgz, r-oldrel (x86_64):POCRE_0.6.0.tgz |
| Old sources: | POCRE archive |
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