Title: Linear Predictive Models Based On The liblinear C/C++ Library.
Author: Thibault Helleputte <thibault.helleputte@dnalytics.com>; Pierre Gramme <pierre.gramme@dnalytics.com>
Description: LiblineaR 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 by LiblineaR. For example: 1.32-14 means that the package supports the version 1.32 of liblinear.

Package: LiblineaR
Title: Linear Predictive Models Based On The Liblinear C/C++ Library.
Version: 1.80-2
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.
Suggests: ROCR
License: GPL-2
Date: 2011-04-11
LazyLoad: yes
URL: http://www.thibaulthelleputte.be
Packaged: 2011-04-13 13:02:19 UTC; thibault
Repository: CRAN
Date/Publication: 2011-04-13 14:23:57