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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: | 2.10-24 |
| Imports: | methods |
| Suggests: | SparseM, Matrix |
| Published: | 2024-09-13 |
| DOI: | 10.32614/CRAN.package.LiblineaR |
| Author: | Thibault Helleputte [cre, aut, cph], Jérôme Paul [aut], Pierre Gramme [aut] |
| Maintainer: | Thibault Helleputte <thibault.helleputte at dnalytics.com> |
| License: | GPL-2 |
| URL: | <https://dnalytics.com/software/liblinear/> |
| NeedsCompilation: | yes |
| Citation: | LiblineaR citation info |
| Materials: | README NEWS |
| In views: | MachineLearning |
| CRAN checks: | LiblineaR results |
| Reference manual: | LiblineaR.pdf |
| Package source: | LiblineaR_2.10-24.tar.gz |
| Windows binaries: | r-devel: LiblineaR_2.10-24.zip, r-release: LiblineaR_2.10-24.zip, r-oldrel: LiblineaR_2.10-24.zip |
| macOS binaries: | r-release (arm64): LiblineaR_2.10-24.tgz, r-oldrel (arm64): LiblineaR_2.10-24.tgz, r-release (x86_64): LiblineaR_2.10-24.tgz, r-oldrel (x86_64): LiblineaR_2.10-24.tgz |
| Old sources: | LiblineaR archive |
| 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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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