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OHPL

Ordered Homogeneity Pursuit Lasso for Group Variable Selection

v1.4.1 · Jul 20, 2024 · GPL-3 | file LICENSE

Description

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.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies glmnet mvtnorm pls OHPL

Version History

new 1.4.1 Mar 9, 2026