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We describe a new library named picasso, which implements a unified frameworkof pathwise coordinate optimization for a variety of sparse learning problems(e.g., sparse linear regression, sparse logistic regression, sparse Poissonregression and scaled sparse linear regression) combined with efficient activeset selection strategies. Besides, the library allows users to choose differentsparsity-inducing regularizers, including the convex $\ell_1$, nonconvex MCPand SCAD regularizers. The library is coded in C++ and has user-friendly R andPython wrappers. Numerical experiments demonstrate that picasso can scale up tolarge problems efficiently.

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python