Linear Predictive Models Based on the LIBLINEAR C/C++ Library R package LiblineaR version 2.10-24 CRAN Task View: MachineLearning (https://CRAN.R-project.org/view=MachineLearning) 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. Software Imports: methods Suggests: SparseM, Matrix Thibault Helleputte <thibault.helleputte@dnalytics.com> Comprehensive R Archive Network (CRAN) Thibault Helleputte [cre, aut, cph], Jérôme Paul [aut], Pierre Gramme [aut] GPL-2 2024-09-13 application/tgz https://CRAN.R-project.org/package=LiblineaR doi:10.32614/CRAN.package.LiblineaR