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# CITATION.cff 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 "featR" in publications use:'
type: software
license: MIT
title: 'featR: A Unified Toolkit for Feature Selection'
version: 0.1.0
doi: 10.32614/CRAN.package.featR
abstract: 'Filter, wrapper, and embedded feature-selection methods behind a consistent
  set of functions that share one calling convention and one return type: correlation
  and chi-squared filters, information gain, LASSO and elastic net, Bayesian model
  comparison, Boruta, recursive feature elimination, random forest importance, multivariate
  adaptive regression splines, support vector machine recursive feature elimination,
  stepwise selection, and principal component / singular value decomposition helpers.
  The implemented methods follow Tibshirani (1996) <https://doi.org/10.1111/j.2517-6161.1996.tb02080.x>,
  Zou and Hastie (2005) <https://doi.org/10.1111/j.1467-9868.2005.00503.x>, Friedman
  (1991) <https://doi.org/10.1214/aos/1176347963>, Breiman (2001) <https://doi.org/10.1023/A:1010933404324>,
  Guyon, Weston, Barnhill and Vapnik (2002) <https://doi.org/10.1023/A:1012487302797>,
  Kursa and Rudnicki (2010) <https://doi.org/10.18637/jss.v036.i11>, and Vehtari,
  Gelman and Gabry (2017) <https://doi.org/10.1007/s11222-016-9696-4>. Heavy modeling
  engines are optional and only required by the functions that use them.'
authors:
- family-names: Chase
  given-names: Justin
  email: jchase.msu@gmail.com
repository: https://elkronos.r-universe.dev
repository-code: https://github.com/elkronos/featR
commit: f744cfd738b1f1431cca3342aa903e26aa6ae280
url: https://elkronos.github.io/featR/
date-released: '2026-09-27'
contact:
- family-names: Chase
  given-names: Justin
  email: jchase.msu@gmail.com