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