bmrm: Bundle Methods for Regularized Risk Minimization Package

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.

Version:	4.1
Depends:	R (≥ 3.0.2)
Imports:	methods, lpSolve, LowRankQP, matrixStats, Rcpp
LinkingTo:	Rcpp
Suggests:	knitr
Published:	2019-04-03
Author:	Julien Prados
Maintainer:	Julien Prados <julien.prados at unige.ch>
License:	GPL-3
Copyright:	2017, University of Geneva
NeedsCompilation:	yes
Materials:	NEWS
In views:	MachineLearning
CRAN checks:	bmrm results

Documentation:

Reference manual:	bmrm.pdf
Vignettes:	bmrm User Guide

Downloads:

Package source:	bmrm_4.1.tar.gz
Windows binaries:	r-devel: bmrm_4.1.zip, r-release: bmrm_4.1.zip, r-oldrel: bmrm_4.1.zip
macOS binaries:	r-release (arm64): bmrm_4.1.tgz, r-oldrel (arm64): bmrm_4.1.tgz, r-release (x86_64): bmrm_4.1.tgz, r-oldrel (x86_64): bmrm_4.1.tgz
Old sources:	bmrm archive

Linking:

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