Package: picasso 1.3.1

Jason Ge

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.

Authors:Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao

picasso_1.3.1.tar.gz
picasso_1.3.1.tar.gz(r-4.5-noble)picasso_1.3.1.tar.gz(r-4.4-noble)
picasso_1.3.1.tgz(r-4.4-emscripten)picasso_1.3.1.tgz(r-4.3-emscripten)
picasso.pdf |picasso.html
picasso/json (API)

# Install 'picasso' in R:
install.packages('picasso', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

3.53 score 1 stars 3 packages 38 scripts 320 downloads 16 exports 3 dependencies

Last updated 6 years agofrom:2422a6e515. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 24 2024
R-4.5-linux-x86_64NOTEDec 24 2024

Exports:coef.gaussiancoef.logitcoef.poissoncoef.sqrtlassopicassoplot.gaussianplot.logitplot.poissonplot.sqrtlassopredict.gaussianpredict.logitpredict.poissonprint.gaussianprint.logitprint.poissonprint.sqrtlasso

Dependencies:latticeMASSMatrix

vignette

Rendered fromvignette.Rnwusingutils::Sweaveon Dec 24 2024.

Last update: 2015-12-18
Started: 2015-12-18