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


Reference manual: picasso.pdf
Vignettes: vignette


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


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