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.


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install.packages("msgl")

2.3.5 by Martin Vincent, 3 months ago


http://dx.doi.org/10.1016/j.csda.2013.06.004, https://github.com/vincent- dk/msgl


Report a bug at https://github.com/vincent-dk/msgl/issues


Browse source code at https://github.com/cran/msgl


Authors: Martin Vincent


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