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.


Reference manual

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1.3.1 by Jason Ge, 2 months ago

Browse source code at

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

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

GPL-3 license

Imports methods

Depends on MASS, Matrix

Imported by sparsevar.

See at CRAN