Package: msgl
Type: Package
Title: High Dimensional Multiclass Classification Using Sparse Group
        Lasso
Version: 2.3.6
Date: 2017-03-31
Author: Martin Vincent
Maintainer: Martin Vincent <martin.vincent.dk@gmail.com>
Description: 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 parallel computing for cross validation and
        subsampling is supported through the 'foreach' and 'doParallel'
        packages. Development version is on GitHub, please report
        package issues on GitHub.
URL:
        http://www.sciencedirect.com/science/article/pii/S0167947313002168,
        https://github.com/vincent-dk/msgl
BugReports: https://github.com/vincent-dk/msgl/issues
License: GPL (>= 2)
LazyLoad: yes
Imports: methods, tools, utils, stats
Depends: R (>= 3.2.4), Matrix, sglOptim (== 1.3.6)
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, BH, sglOptim
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2017-04-02 13:37:27 UTC; martin
Repository: CRAN
Date/Publication: 2017-04-02 17:41:17 UTC
Built: R 3.2.5; x86_64-w64-mingw32; 2017-04-20 07:54:53 UTC; windows
Archs: i386, x64
