msgl (2.2.0)

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.

Maintainer: Martin Vincent
Author(s): Martin Vincent

License: GPL (>= 2)

Uses: Matrix, sglOptim

Released about 4 years ago.