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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.
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Package details |
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| Author | You-Wu Lin [aut], Nan Xiao [aut, cre] (<https://orcid.org/0000-0002-0250-5673>) |
| Maintainer | Nan Xiao <me@nanx.me> |
| License | GPL-3 | file LICENSE |
| Version | 1.4.1 |
| URL | https://ohpl.io https://ohpl.io/doc/ https://github.com/nanxstats/OHPL |
| Package repository | View on CRAN |
| Installation |
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