OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection
Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <doi:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.
| Version: | 1.4 |
| Depends: | R (≥ 3.0.2) |
| Imports: | glmnet, pls, mvtnorm |
| Published: | 2019-05-18 |
| Author: | You-Wu Lin [aut],
Nan Xiao  [cre] |
| Maintainer: | Nan Xiao <me at nanx.me> |
| BugReports: | https://github.com/nanxstats/OHPL/issues |
| License: | GPL-3 | file LICENSE |
| URL: | https://ohpl.io, https://ohpl.io/doc/, https://github.com/nanxstats/OHPL |
| NeedsCompilation: | no |
| Citation: | OHPL citation info |
| Materials: | README NEWS |
| CRAN checks: | OHPL results |
Documentation:
| Reference manual: | OHPL.pdf |
Downloads:
| Package source: | OHPL_1.4.tar.gz |
| Windows binaries: | r-devel: OHPL_1.4.zip, r-release: OHPL_1.4.zip, r-oldrel: OHPL_1.4.zip |
| macOS binaries: | r-release (arm64): OHPL_1.4.tgz, r-oldrel (arm64): OHPL_1.4.tgz, r-release (x86_64): OHPL_1.4.tgz, r-oldrel (x86_64): OHPL_1.4.tgz |
| Old sources: | OHPL archive |
Linking:
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