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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 "LiblineaR" in publications use:'
type: software
license: GPL-2.0-only
title: 'LiblineaR: Linear Predictive Models Based on the LIBLINEAR C/C++ Library'
version: 2.10-26
identifiers:
- type: doi
  value: 10.32614/CRAN.package.LiblineaR
abstract: A wrapper around the LIBLINEAR C/C++ library for machine learning (available
  at <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a simple library
  for solving large-scale regularized linear classification and regression. It currently
  supports L2-regularized classification (such as logistic regression, L2-loss linear
  SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss
  linear SVM and logistic regression) and L2-regularized support vector regression
  (with L1- or L2-loss). The main features of LiblineaR include multi-class classification
  (one-vs-the rest, and Crammer & Singer method), cross validation for model selection,
  probability estimates (logistic regression only) or weights for unbalanced data.
  The estimation of the models is particularly fast as compared to other libraries.
authors:
- family-names: Helleputte
  given-names: Thibault
  email: thibault.helleputte@dnalytics.com
- family-names: Paul
  given-names: Jérôme
- family-names: Gramme
  given-names: Pierre
preferred-citation:
  type: manual
  title: 'LiblineaR: Linear Predictive Models Based on the LIBLINEAR C/C++ Library'
  authors:
  - family-names: Helleputte
    given-names: Thibault
    email: thibault.helleputte@dnalytics.com
  - family-names: Paul
    given-names: Jérôme
  - family-names: Gramme
    given-names: Pierre
  year: '2026'
  notes: R package version 2.10-26
repository: https://cran.r-universe.dev
commit: 9462ebc24513ddf13f8c4909f37b2889c108908e
date-released: '2026-09-24'
contact:
- family-names: Helleputte
  given-names: Thibault
  email: thibault.helleputte@dnalytics.com