Multinomial logistic regression with sparse group lasso penalty. Suitable for high dimensional multiclass classification with many classes. The algorithm finds the sparse group lasso penalized maximum likelihood estimator. This result in feature and parameter selection, and parameter estimation. Use of multiple processors for cross validation and subsampling is supported through OpenMP. Development version is on github.
| Version: | 2.2.0 |
| Depends: | R (≥ 3.0.0), Matrix, sglOptim (== 1.2.0) |
| Imports: | methods, utils, stats |
| LinkingTo: | Rcpp, RcppProgress, RcppArmadillo, BH, sglOptim |
| Published: | 2015-09-19 |
| Author: | Martin Vincent |
| Maintainer: | Martin Vincent <vincent at math.ku.dk> |
| 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 |
| Reference manual: | msgl.pdf |
| Package source: | msgl_2.2.0.tar.gz |
| Windows binaries: | r-devel: msgl_2.2.0.zip, r-release: msgl_2.2.0.zip, r-oldrel: msgl_2.2.0.zip |
| OS X Snow Leopard binaries: | r-release: msgl_2.2.0.tgz, r-oldrel: msgl_2.0.125.1.tgz |
| OS X Mavericks binaries: | r-release: msgl_2.2.0.tgz |
| Old sources: | msgl archive |