picasso: Sparse Learning with Convex and Concave Penalties

Fast tools for fitting sparse generalized linear models with convex penalties (lasso) and concave penalties (smoothly clipped absolute deviation and minimax concave penalty). Computation uses multi-stage convex relaxation and pathwise coordinate optimization with warm starts, active-set updates, and screening rules. Core solvers are implemented in C++, and coefficient paths are stored as sparse matrices for memory efficiency.

Version:	1.4.1
Depends:	R (≥ 2.15.0), MASS, Matrix
Imports:	methods
Published:	2026-03-10
DOI:	10.32614/CRAN.package.picasso
Author:	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] (Contributor to bundled Eigen headers), Benoit Jacob [ctb] (Contributor to bundled Eigen headers), Eigen Library Authors [cph] (Copyright holders of bundled Eigen headers in src/include/eigen3)
Maintainer:	Tuo Zhao <tourzhao at gatech.edu>
License:	GPL-3
Copyright:	See inst/COPYRIGHTS for bundled third-party copyright and license notices. picasso copyright details
NeedsCompilation:	yes
CRAN checks:	picasso results

Documentation:

Reference manual:	picasso.html , picasso.pdf
Vignettes:	vignette (source)

Downloads:

Package source:	picasso_1.4.1.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): picasso_1.4.1.tgz, r-oldrel (arm64): picasso_1.4.1.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available
Old sources:	picasso archive

Linking:

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