Encoding: UTF-8
Package: LiblineaR
Title: Linear Predictive Models Based on the 'LIBLINEAR' C/C++ Library
Version: 2.10-8
Date: 2017-02-13
Author: Thibault Helleputte <thibault.helleputte@dnalytics.com>; Pierre
    Gramme <pierre.gramme@dnalytics.com>; Jerome Paul
    <jerome.paul@dnalytics.com>
Maintainer: Thibault Helleputte <thibault.helleputte@dnalytics.com>
Description: A wrapper around the 'LIBLINEAR' C/C++ library for machine
    learning (available at
    <http://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.
License: GPL-2
LazyLoad: yes
Suggests: SparseM
URL: http://dnalytics.com/liblinear/
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2017-02-13 09:17:41 UTC; jey
Repository: CRAN
Date/Publication: 2017-02-13 12:58:40
Built: R 4.1.0; x86_64-w64-mingw32; 2020-07-30 05:24:43 UTC; windows
Archs: i386, x64
