OHPL-package: OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable...

In OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Description

Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) \Sexpr[results=rd]{tools:::Rd_expr_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 [e-mail] (ORCID)

Authors:

• You-Wu Lin [e-mail]

See Also

Useful links:

• https://ohpl.io

• https://ohpl.io/doc/

• https://github.com/nanxstats/OHPL

• Report bugs at https://github.com/nanxstats/OHPL/issues

OHPL documentation built on Sept. 11, 2024, 7:05 p.m.