Package: LiblineaR 2.10-24

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.

Authors:Thibault Helleputte [cre, aut, cph], Jérôme Paul [aut], Pierre Gramme [aut]

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NEWS

# Install 'LiblineaR' in R:
install.packages('LiblineaR', repos = c('https://dnalytics.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.98 score 11 packages 275 scripts 5.3k downloads 15 mentions 2 exports 0 dependencies

Last updated 2 months agofrom:07cca10ee7. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-win-x86_64OKNov 13 2024
R-4.5-linux-x86_64OKNov 13 2024
R-4.4-win-x86_64OKNov 13 2024
R-4.4-mac-x86_64OKNov 13 2024
R-4.4-mac-aarch64OKNov 13 2024
R-4.3-win-x86_64OKNov 13 2024
R-4.3-mac-x86_64OKNov 13 2024
R-4.3-mac-aarch64OKNov 13 2024

Exports:heuristicCLiblineaR

Dependencies: