LiblineaR: Linear Predictive Models Based on the LIBLINEAR C/C++ Library

A wrapper around the LIBLINEAR C/C++ library for machine learning (available at <>). 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.

Version: 2.10-23
Imports: methods
Suggests: SparseM, Matrix
Published: 2023-12-11
DOI: 10.32614/CRAN.package.LiblineaR
Author: Thibault Helleputte [cre, aut, cph], Jérôme Paul [aut], Pierre Gramme [aut]
Maintainer: Thibault Helleputte <thibault.helleputte at>
License: GPL-2
URL: <>
NeedsCompilation: yes
Citation: LiblineaR citation info
Materials: README NEWS
In views: MachineLearning
CRAN checks: LiblineaR results


Reference manual: LiblineaR.pdf


Package source: LiblineaR_2.10-23.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): LiblineaR_2.10-23.tgz, r-oldrel (arm64): LiblineaR_2.10-23.tgz, r-release (x86_64): LiblineaR_2.10-23.tgz, r-oldrel (x86_64): LiblineaR_2.10-23.tgz
Old sources: LiblineaR archive

Reverse dependencies:

Reverse depends: LKT
Reverse imports: ILoReg, kebabs, PrInCE, quanteda.textmodels, scBio, SIAMCAT, sweater
Reverse suggests: flowml, mlr, parsnip, RSSL, tidyAML, vetiver


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