Package: picasso 2.0.1

picasso: Sparse Learning with Convex and Concave Penalties

Fast regularization paths for sparse Gaussian, binomial, Poisson, square-root-lasso, and multinomial models with lasso, smoothly clipped absolute deviation, or minimax concave penalties. Computation uses pathwise coordinate optimization, active-set updates, warm starts, screening rules, Proximal Newton iterations, quadratic majorization, and adaptive local linear approximation where appropriate. Core solvers are implemented in C++, and coefficient paths are returned as Matrix-compatible objects.

Authors:Jason Ge [aut], Xingguo Li [aut], Haoming Jiang [aut], Mengdi Wang [aut], Tong Zhang [aut], Han Liu [aut], Tuo Zhao [aut, cre], Gael Guennebaud [ctb], Benoit Jacob [ctb], Eigen Library Authors [cph]