Package: picasso
Type: Package
Title: Pathwise Calibrated Sparse Shooting Algorithm
Version: 1.3.1
Date: 2019-02-10
Author: Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao
Maintainer: Jason Ge <jiange@princeton.edu>
Depends: R (>= 2.15.0), MASS, Matrix
Imports: methods
Description: 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.
License: GPL-3
Repository: CRAN
Packaged: 2019-02-21 17:39:23 UTC; Jason
NeedsCompilation: yes
Date/Publication: 2019-02-21 21:20:18 UTC
Built: R 3.5.3; x86_64-w64-mingw32; 2020-04-22 05:07:27 UTC; windows
Archs: i386, x64
