picasso-package: picasso: Sparse Learning with Convex and Non-Convex Penalties

In picasso: Sparse Learning with Convex and Concave Penalties

picasso: Sparse Learning with Convex and Non-Convex Penalties

Description

Computationally efficient tools for fitting sparse generalized linear models with convex or non-convex penalties. Supported penalties include lasso, smoothly clipped absolute deviation (SCAD), and minimax concave penalty (MCP). Computation is based on multi-stage convex relaxation and pathwise coordinate optimization with warm starts, active-set updates, and screening rules.

Details

Core optimization routines are implemented in C++ for speed, and coefficient paths are stored as sparse matrices for memory efficiency.

Author(s)

Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao
Maintainer: Tuo Zhao <[e-mail]>

See Also

picasso.

picasso documentation built on March 12, 2026, 5:06 p.m.