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:	1.4.1
Depends:	R (≥ 3.0.2)
Imports:	glmnet, mvtnorm, pls
Published:	2024-07-20
DOI:	10.32614/CRAN.package.OHPL
Author:	You-Wu Lin [aut], Nan Xiao [aut, 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_1.4.1.tar.gz
Windows binaries:	r-devel: OHPL_1.4.1.zip, r-release: OHPL_1.4.1.zip, r-oldrel: OHPL_1.4.1.zip
macOS binaries:	r-release (arm64): OHPL_1.4.1.tgz, r-oldrel (arm64): OHPL_1.4.1.tgz, r-release (x86_64): OHPL_1.4.1.tgz, r-oldrel (x86_64): OHPL_1.4.1.tgz
Old sources:	OHPL archive

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