doi:10.18637/jss.v110.i06>. It supports the use of a sparse design matrix as well as returning coefficient estimates in a sparse matrix. Furthermore, it correctly calculates the degrees of freedom to allow for information criteria rather than cross-validation with very large data. Finally, the interface to compiled code avoids unnecessary copies and allows for the use of long integers.">

sparsegl: Sparse Group Lasso (original) (raw)

Author:

Daniel J. McDonald [aut, cre], Xiaoxuan Liang [aut], Anibal Solón Heinsfeld [aut], Aaron Cohen [aut], Yi Yang [ctb], Hui Zou [ctb], Jerome Friedman [ctb], Trevor Hastie [ctb], Rob Tibshirani [ctb], Balasubramanian Narasimhan [ctb], Kenneth Tay [ctb], Noah Simon [ctb], Junyang Qian [ctb], James Yang [ctb]