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)

Peer review:

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

16 exports 1 stars 1.08 score 3 dependencies 3 dependents 38 scripts 324 downloads

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

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

Dependencies:latticeMASSMatrix