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 |
| 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 |
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