seagull

lasso, group lasso and sparse-group lasso regularisation for linear regression models via proximal gradient descent. This package provides regularization paths for the lasso, group lasso, and sparse-group lasso. The underlying mathematical model is a mixed model, i.e., a model with fixed and random effects. (Whereas it is actually optional to include any fixed effect.). Lasso, Group Lasso, and Sparse-Group Lasso for Mixed Models. Proximal gradient descent solver for the operators lasso, group lasso, and sparse-group lasso. The implementation involves backtracking line search and warm starts. Input data needs to be clustered/grouped for the (sparse-)group lasso before calling these algorithms. The sparse-group lasso contains two penalty terms, which are combined via a mixing parameter 0 <= alpha <= 1. The lasso, group lasso, and sparse-group lasso are implemented via proximal gradient descent.