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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "OHPL" in publications use:'
type: software
license: GPL-3.0-only
title: 'OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection'
version: 1.4.1
doi: 10.1016/j.chemolab.2017.07.004
identifiers:
- type: doi
  value: 10.32614/CRAN.package.OHPL
- type: url
  value: https://ohpl.io/doc/
abstract: 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.
authors:
- family-names: Lin
  given-names: You-Wu
  email: lyw015813@126.com
- family-names: Xiao
  given-names: Nan
  email: me@nanx.me
  orcid: https://orcid.org/0000-0002-0250-5673
preferred-citation:
  type: article
  title: Ordered homogeneity pursuit lasso for group variable selection with applications
    to spectroscopic data
  authors:
  - family-names: Lin
    given-names: You-Wu
    email: lyw015813@126.com
  - family-names: Xiao
    given-names: Nan
    email: me@nanx.me
    orcid: https://orcid.org/0000-0002-0250-5673
  - family-names: Wang
    given-names: Li-Li
  - family-names: Li
    given-names: Chuan-Quan
  - family-names: Xu
    given-names: Qing-Song
  journal: Chemometrics and Intelligent Laboratory Systems
  volume: '168'
  year: '2017'
  doi: 10.1016/j.chemolab.2017.07.004
  start: '62'
  end: '71'
repository: https://nanxstats.r-universe.dev
repository-code: https://github.com/nanxstats/OHPL
commit: 4046c1273714f98070430538bf69738162806cff
url: https://ohpl.io
contact:
- family-names: Xiao
  given-names: Nan
  email: me@nanx.me
  orcid: https://orcid.org/0000-0002-0250-5673