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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 "KRLS" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'KRLS: Kernel-Based Regularized Least Squares'
version: 1.0-0
doi: 10.18637/jss.v079.i03
identifiers:
- type: doi
  value: 10.32614/CRAN.package.KRLS
- type: url
  value: https://www.stanford.edu/~jhain/
abstract: Package implements Kernel-based Regularized Least Squares (KRLS), a machine
  learning method to fit multidimensional functions y=f(x) for regression and classification
  problems without relying on linearity or additivity assumptions. KRLS finds the
  best fitting function by minimizing the squared loss of a Tikhonov regularization
  problem, using Gaussian kernels as radial basis functions. For further details see
  Hainmueller and Hazlett (2014).
authors:
- family-names: Hainmueller
  given-names: Jens
  email: jhain@stanford.edu
preferred-citation:
  type: article
  title: Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)
  authors:
  - family-names: Ferwerda
    given-names: Jeremy
  - family-names: Hainmueller
    given-names: Jens
    email: jhain@stanford.edu
  - family-names: Hazlett
    given-names: Chad J.
  journal: Journal of Statistical Software
  year: '2017'
  volume: '79'
  issue: '3'
  doi: 10.18637/jss.v079.i03
  start: '1'
  end: '26'
repository: https://jankee2022.r-universe.dev
commit: 9ee4980f0f951956294c84b7f49fe4374e5a432b
url: https://www.r-project.org
date-released: '2017-07-08'
contact:
- family-names: Hainmueller
  given-names: Jens
  email: jhain@stanford.edu