Package: msgl
Type: Package
Title: High Dimensional Multiclass Classification Using Sparse Group
        Lasso
Version: 2.3.0
Date: 2016-09-28
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://dx.doi.org/10.1016/j.csda.2013.06.004,
        https://github.com/vincent-dk/msgl
BugReports: https://github.com/vincent-dk/msgl/issues
License: GPL (>= 2)
LazyLoad: yes
Imports: methods, utils, stats
Depends: R (>= 3.0.0), Matrix, sglOptim (== 1.3.0)
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, BH, sglOptim
Suggests: knitr
VignetteBuilder: knitr
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-09-28 17:01:28 UTC; martin
Repository: CRAN
Date/Publication: 2016-09-28 19:35:07
Built: R 3.3.2; x86_64-w64-mingw32; 2016-12-02 03:30:45 UTC; windows
Archs: i386, x64
