OHPL-package {OHPL}R Documentation

OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Description

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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(s)

Maintainer: Nan Xiao me@nanx.me (ORCID)

Authors:

See Also

Useful links:


[Package OHPL version 1.4.1 Index]