msgl (2.3.6)

High Dimensional Multiclass Classification Using Sparse Group Lasso.

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.

Maintainer: Martin Vincent
Author(s): Martin Vincent

License: GPL (>= 2)

Uses: Matrix, sglOptim, knitr, rmarkdown

Released over 2 years ago.