Martin Vincent (Sparse group lasso), Conrad Sanderson
(Armadillo), Jaakko Jarvi (Boost Tuple) and Jens Maurer (Boost
Random)
Sparse group lasso multiclass classification, suitable for
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
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 (see url).
http://arxiv.org/abs/1205.1245 http://arma.sourceforge.net/,
http://www.mozilla.org/MPL/2.0/, http://www.boost.org/,
http://www.boost.org/users/license.html

R (>= 2.13.0), Matrix, RcppProgress, RcppArmadillo, BH

R (>= 3.0.0), Matrix, RcppProgress, RcppArmadillo, BH

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.

Rcpp, RcppProgress, RcppArmadillo, BH, sglOptim
'msgl_fit.R' 'msgl_cv.R' 'msgl_subsampling.R' 'msgl_predict.R'
'msgl_navigate.R' 'msgl_config.R'

High Dimensional Multiclass Classification Using Sparse Group
Lasso

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.