Package: LiblineaR
Title: Linear Predictive Models Based On The Liblinear C/C++ Library.
Version: 1.80-7
Author: Thibault Helleputte <thelleputte@gmail.com>
Maintainer: Thibault Helleputte <thelleputte@gmail.com>
Description: This R package is a wrapper around the liblinear C/C++
library for machine learning. LIBLINEAR is a linear classifier
for data with millions of instances and features. It supports
L2-regularized classifiers (such as L2-loss linear SVM, L1-loss
linear SVM, and logistic regression) as well as L1-regularized
classifiers (such as L2-loss linear SVM and logistic
regression). 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. For more information on the C/C++ liblinear
library itself, refer to R.-E. Fan, K.-W. Chang, C.-J. Hsieh,
X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large
Linear Classification, Journal of Machine Learning Research
9(2008), 1871-1874, available at
http://www.csie.ntu.edu.tw/~cjlin/liblinear . The two first
blocks of the package version indicates which version of
liblinear is currently supported. For example: 1.32-14 means
that the package supports the version 1.32 of liblinear.
License: GPL-2
Date: 2013-06-24
LazyLoad: yes
Suggests: SparseM
URL: http://www.thibaulthelleputte.be
Packaged: 2013-06-24 15:27:29 UTC; thibault
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-06-24 17:41:20

Package: LiblineaR
Title: Linear Predictive Models Based on the LIBLINEAR C/C++ Library
Version: 1.94-2
Author: Thibault Helleputte <thibault.helleputte@dnalytics.com>; Pierre Gramme
<pierre.gramme@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
Date: 2015-01-30
LazyLoad: yes
Suggests: SparseM
URL: http://dnalytics.com/liblinear/
Packaged: 2015-02-04 06:31:47 UTC; thibault
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-02-04 08:28:04