msgl: High Dimensional Multiclass Classification Using Sparse Group Lasso

Multinomial logistic regression with sparse group lasso penalty. Simultaneous feature selection and parameter estimation for classification. Suitable for high dimensional multiclass classification with many classes. The algorithm computes the sparse group lasso penalized maximum likelihood estimate. Use of multiple processors for cross validation and subsampling is supported through OpenMP. Development version is on github, please report package issues on github.

Version: 2.2.1
Depends: R (≥ 3.0.0), Matrix, sglOptim (== 1.2.2)
Imports: methods, utils, stats
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, BH, sglOptim
Suggests: knitr
Published: 2016-09-10
Author: Martin Vincent
Maintainer: Martin Vincent <martin.vincent.dk at gmail.com>
BugReports: https://github.com/vincent-dk/msgl/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://dx.doi.org/10.1016/j.csda.2013.06.004 https://github.com/vincent-dk/ msgl
NeedsCompilation: yes
Citation: msgl citation info
Materials: NEWS
CRAN checks: msgl results

Downloads:

Reference manual: msgl.pdf
Vignettes: Quick Start
Package source: msgl_2.2.1.tar.gz
Windows binaries: r-devel: msgl_2.2.1.zip, r-release: msgl_2.2.1.zip, r-oldrel: msgl_2.2.1.zip
OS X Mavericks binaries: r-release: msgl_2.2.1.tgz, r-oldrel: msgl_2.2.1.tgz
Old sources: msgl archive

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