Package: picasso
Type: Package
Title: Sparse Learning with Convex and Concave Penalties
Version: 1.5
Date: 2026-02-18
Maintainer: Tuo Zhao <tourzhao@gatech.edu>
Authors@R: c(
 person("Jason", "Ge", role = "aut", email = "jiange@princeton.edu"),
 person("Xingguo", "Li", role = "aut"),
 person("Haoming", "Jiang", role = "aut"),
 person("Mengdi", "Wang", role = "aut"),
 person("Tong", "Zhang", role = "aut"),
 person("Han", "Liu", role = "aut"),
 person("Tuo", "Zhao", role = c("aut", "cre"), email = "tourzhao@gatech.edu"),
 person("Gael", "Guennebaud", role = "ctb",
        comment = "Contributor to bundled Eigen headers"),
 person("Benoit", "Jacob", role = "ctb",
        comment = "Contributor to bundled Eigen headers"),
 person("Eigen", "Library Authors", role = "cph",
        comment = "Copyright holders of bundled Eigen headers in src/include/eigen3")
 )
Depends: R (>= 2.15.0), MASS, Matrix
Imports: methods
Description: 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.
License: GPL-3
Copyright: See inst/COPYRIGHTS for bundled third-party copyright and
        license notices.
Repository: CRAN
NeedsCompilation: yes
Packaged: 2026-03-11 03:50:51 UTC; tourzhao
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)
Date/Publication: 2026-03-12 14:00:02 UTC
Built: R 4.5.3; x86_64-w64-mingw32; 2026-06-06 02:10:00 UTC; windows
Archs: x64
