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
DOI: 10.32614/CRAN.package.OHPL
Author: You-Wu Lin [aut], Nan Xiao ORCID iD [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


Reference manual: OHPL.pdf


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


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