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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 "pengls" in publications use:'
type: software
license: GPL-2.0-only
title: 'pengls: Fit Penalised Generalised Least Squares models'
version: 1.18.0
doi: 110.3389/fpls.2022.858711
identifiers:
- type: doi
  value: 10.32614/CRAN.package.pengls
abstract: Combine generalised least squares methodology from the nlme package for
  dealing with autocorrelation with penalised least squares methods from the glmnet
  package to deal with high dimensionality. This pengls packages glues them together
  through an iterative loop. The resulting method is applicable to high dimensional
  datasets that exhibit autocorrelation, such as spatial or temporal data.
authors:
- family-names: Hawinkel
  given-names: Stijn
  email: stijn.hawinkel@psb.ugent.be
  orcid: https://orcid.org/0000-0002-4501-5180
preferred-citation:
  type: article
  title: 'Spatial Regression Models for Field Trials: A Comparative Study and New
    Ideas'
  authors:
  - family-names: Hawinkel
    given-names: Stijn
    email: stijn.hawinkel@psb.ugent.be
    orcid: https://orcid.org/0000-0002-4501-5180
  - family-names: De Meyer
    given-names: Sam
  - family-names: Maere
    given-names: Steven
  journal: Frontiers in Plant Science
  year: '2022'
  doi: 110.3389/fpls.2022.858711
repository: https://bioc-release.r-universe.dev
repository-code: https://github.com/sthawinke/pengls
commit: b8b4f18d0f840c14a3e9a27128f4e2effb20abba
url: https://github.com/sthawinke/pengls
date-released: '2026-04-28'
contact:
- family-names: Hawinkel
  given-names: Stijn
  email: stijn.hawinkel@psb.ugent.be
  orcid: https://orcid.org/0000-0002-4501-5180