The picasso Package for High Dimensional Regularized Sparse Learning in R

We introduce an R package named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (Sparse Linear Regression, Sparse Logistic Regression and Sparse Poisson Regression), combined with efficient active set selection strategies. Besides, the package allows users to choose different sparsityinducing regularizers, including the convex `1, nonvoncex MCP and SCAD regularizers. The package is coded in C and can scale up to large problems efficiently with the memory optimized using sparse matrix output.