picasso: Pathwise Calibrated Sparse Shooting Algorithm

Computationally efficient tools for fitting generalized linear model with convex or non-convex penalty. Users can enjoy the superior statistical property of non-convex penalty such as SCAD and MCP which has significantly less estimation error and overfitting compared to convex penalty such as lasso and ridge. Computation is handled by multi-stage convex relaxation and the PathwIse CAlibrated Sparse Shooting algOrithm (PICASSO) which exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. The computation is memory-optimized using the sparse matrix output.

Version:	1.3.1
Depends:	R (≥ 2.15.0), MASS, Matrix
Imports:	methods
Published:	2019-02-21
DOI:	10.32614/CRAN.package.picasso
Author:	Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao
Maintainer:	Jason Ge <jiange at princeton.edu>
License:	GPL-3
NeedsCompilation:	yes
In views:	MachineLearning
CRAN checks:	picasso results [issues need fixing before 2024-12-25]

Documentation:

Reference manual:	picasso.pdf
Vignettes:	vignette

Downloads:

Package source:	picasso_1.3.1.tar.gz
Windows binaries:	r-devel: picasso_1.3.1.zip, r-release: picasso_1.3.1.zip, r-oldrel: picasso_1.3.1.zip
macOS binaries:	r-release (arm64): picasso_1.3.1.tgz, r-oldrel (arm64): picasso_1.3.1.tgz, r-release (x86_64): picasso_1.3.1.tgz, r-oldrel (x86_64): picasso_1.3.1.tgz
Old sources:	picasso archive

Reverse dependencies:

Reverse imports:

sparsevar

Linking:

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