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msgl (version 2.0.125.0)

High dimensional multiclass classification using sparse group lasso

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.

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Version

Install

install.packages('msgl')

Monthly Downloads

64

Version

2.0.125.0

License

GPL (>= 2)

Maintainer

Martin Vincent

Last Published

March 26th, 2014

Functions in msgl (2.0.125.0)

coef.msgl

Extract nonzero coefficients
msgl

Fit a multinomial sparse group lasso regularization path.
msgl.lambda.seq

Computes a lambda sequence for the regularization path
Err.msgl

Compute error rates
nmod.msgl

Returns the number of models in a msgl object
msgl.cv

Multinomial sparse group lasso cross validation
sim.data

Simulated data set
msgl.standard.config

Standard msgl algorithm configuration
predict.msgl

Predict
parameters.msgl

Nonzero parameters
features.msgl

Nonzero features
print.msgl

Print function for msgl
models.msgl

Exstract the fitted models
msgl.algorithm.config

Create a new algorithm configuration
msgl.subsampling

Multinomial sparse group lasso generic subsampling procedure