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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 imports: | sparsevar |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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