Author: Martin Vincent (Sparse group lasso), Conrad Sanderson
(Armadillo), Jaakko Jarvi (Boost Tuple) and Jens Maurer (Boost
high dimensional problems with many classes (see url). Fast
algorithm for solving the multinomial sparse group lasso
optimization problem. Use of multiple processors for cross
validation and subsampling is supported through OpenMP. The
Armadillo C++ library is used as the primary linear algebra
engine. Armadillo is licensed under the MPL 2.0 (see url). The
licensed under the Boost Software License (see url).

Package: msgl
Type: Package
Title: High dimensional multiclass classification using sparse group
lasso
Version: 0.1.3
Date: 2013-20-05
Author: Martin Vincent
Maintainer: Martin Vincent <vincent@math.ku.dk>
Description: Sparse group lasso multiclass classification, suitable for
high dimensional problems with many classes. Fast algorithm for
solving the multinomial sparse group lasso convex optimization
problem. This package apply template metaprogramming
techniques, therefore -- when compiling the package from source
-- a high level of optimization is needed to gain full speed
(e.g. for the GCC compiler use -O3). Use of multiple processors
for cross validation and subsampling is supported through
OpenMP. The Armadillo C++ library is used as the primary linear
algebra engine. Armadillo is licensed under the MPL 2.0. The
Armadillo C++ library is primarily developed at NICTA
(Australia) by Conrad Sanderson, with contributions from around
the world. Furthermore the package utilize various Boost
libraries, in particular the Tuple library by Jaakko Jarvi and
the Random library by Jens Maurer. The Boost libraries are
licensed under the Boost Software License.
URL: http://arxiv.org/abs/1205.1245 http://arma.sourceforge.net/,
http://www.boost.org/
License: GPL (>= 2)
LazyLoad: yes
Depends: R (>= 2.13.0), Matrix, RcppProgress, RcppArmadillo, BH
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, BH
Collate: 'msgl_multinomial.R'
Packaged: 2013-05-27 16:50:51 UTC; martin
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-05-27 22:45:46