LiblineaR: Linear Predictive Models Based on the LIBLINEAR C/C++ Library
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.
| Version: |
2.10-25 |
| Imports: |
methods |
| Suggests: |
SparseM, Matrix, testthat (≥ 3.0.0), knitr, rmarkdown, spelling |
| Published: |
2026-09-11 |
| DOI: |
10.32614/CRAN.package.LiblineaR |
| Author: |
Thibault Helleputte [cre, aut, cph],
Jérôme Paul [aut],
Pierre Gramme [aut],
Chih-Jen Lin [cph] (Author of the bundled LIBLINEAR C/C++ code
(src/linear.cpp, linear.h, tron.cpp, tron.h); see LICENSE.note) |
| Maintainer: |
Thibault Helleputte <thibault.helleputte at dnalytics.com> |
| License: |
GPL-2 |
| URL: |
<https://www.dnalytics.com/publications> |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Citation: |
LiblineaR citation info |
| Materials: |
README, NEWS |
| In views: |
MachineLearning |
| CRAN checks: |
LiblineaR results |
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
LKT |
| Reverse imports: |
Coralysis, ILoReg, kebabs, PrInCE, scBio, SIAMCAT, sweater |
| Reverse suggests: |
mlr, orbital, parsnip, postcard, RSSL, tidyAML, tidypredict, vetiver |
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