Multinomial 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.


Reference manual

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2.3.9 by Niels Richard Hansen, 2 years ago,

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Authors: Martin Vincent [aut] , Niels Richard Hansen [ctb, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports methods, tools, utils, stats

Depends on Matrix, sglOptim

Suggests knitr, rmarkdown

Linking to Rcpp, RcppProgress, RcppArmadillo, BH, sglOptim

See at CRAN