OHPL
1.4.2Ordered Homogeneity Pursuit Lasso for Group Variable Selection
Overview
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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- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 2.5 median / 7 max
- Documented parameters
- 100%
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10 7 exported
Complexity
2.8 avg / 7 max
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10 nodes / 7 edges
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People & History
5 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.4.2Latest
- RR 4.5.0 released · 2025-04-11
- 1.4.12024-07-20 · diff ↗
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- 1.42019-05-18 · diff ↗
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 1.32017-08-08 · diff ↗
- 1.22017-07-17
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-07-17
- Total releases
- 5 / 9 yrs
- License
- GPL-3 | file LICENSE OSI
- Minimum R
- ≥ 3.0.2
- Bundled data
- 579 KB / 3 files
- Download size
- 641 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet