Ordered Homogeneity Pursuit Lasso for Group Variable Selection R package OHPL version 1.4.2 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. Software Depends: R (>= 3.0.2) Imports: glmnet, mvtnorm, pls Nan Xiao <me@nanx.me> Comprehensive R Archive Network (CRAN) You-Wu Lin [aut], Nan Xiao [aut, cre] (ORCID: <https://orcid.org/0000-0002-0250-5673>) GPL-3 file LICENSE (https://CRAN.R-project.org/package=OHPL/LICENSE) 2026-04-13 application/tgz https://CRAN.R-project.org/package=OHPL doi:10.32614/CRAN.package.OHPL