Package: bmrm
Type: Package
Title: Bundle Methods for Regularized Risk Minimization Package
Version: 3.7
Date: 2018-01-22
Depends: R (>= 3.0.2)
Imports: lpSolve, LowRankQP, matrixStats
Suggests: knitr
VignetteBuilder: knitr
Author: Julien Prados
Maintainer: Julien Prados <julien.prados@unige.ch>
Copyright: 2017, University of Geneva
Description: Bundle methods for minimization of convex and non-convex risk
    under L1 or L2 regularization. Implements the algorithm proposed by Teo et
    al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR
    2012). The package comes with lot of loss functions for machine learning
    which make it powerful for big data analysis. The applications includes:
    structured prediction, linear SVM, multi-class SVM, f-beta optimization,
    ROC optimization, ordinal regression, quantile regression,
    epsilon insensitive regression, least mean square, logistic regression,
    least absolute deviation regression (see package examples), etc... all with
    L1 and L2 regularization.
License: GPL-3
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-02-19 12:15:26 UTC; juju
Repository: CRAN
Date/Publication: 2018-02-19 12:24:20 UTC
