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bmrm  

Bundle Methods for Regularized Risk Minimization Package
View on CRAN: Click here


Download and install bmrm package within the R console
Install from CRAN:
install.packages("bmrm")

Install from Github:
library("remotes")
install_github("cran/bmrm")

Install by package version:
library("remotes")
install_version("bmrm", "4.1")



Attach the package and use:
library("bmrm")
Maintained by
Julien Prados
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2013-05-14
Latest Update: 2023-04-14
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.
How to cite:
Julien Prados (2013). bmrm: Bundle Methods for Regularized Risk Minimization Package. R package version 4.1, https://cran.r-project.org/web/packages/bmrm. Accessed 04 May. 2025.
Previous versions and publish date:
1.3 (2013-05-14 10:07), 1.4 (2013-07-19 16:41), 1.5 (2013-08-06 21:39), 1.6 (2013-09-04 16:47), 1.7 (2014-01-28 16:56), 1.8 (2014-02-10 17:51), 3.0 (2015-01-15 16:59), 3.3 (2017-05-19 23:24), 3.4 (2017-08-16 22:09), 3.7 (2018-02-19 13:24), 4.1 (2019-04-03 16:00)
Other packages that cited bmrm R package
View bmrm citation profile
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Downloads during the last 30 days
04/0404/0604/0704/1204/1704/2104/2204/2604/28Downloads for bmrm02468101214TrendBars

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