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picasso: Sparse Learning with Convex and Concave Penalties
Fast tools for fitting sparse generalized linear models with convex penalties (lasso) and concave penalties (smoothly clipped absolute deviation and minimax concave penalty). Computation uses multi-stage convex relaxation and pathwise coordinate optimization with warm starts, active-set updates, and screening rules. Core solvers are implemented in C++, and coefficient paths are stored as sparse matrices for memory efficiency.
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Package details |
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| Author | Jason Ge [aut], Xingguo Li [aut], Haoming Jiang [aut], Mengdi Wang [aut], Tong Zhang [aut], Han Liu [aut], Tuo Zhao [aut, cre], Gael Guennebaud [ctb] (Contributor to bundled Eigen headers), Benoit Jacob [ctb] (Contributor to bundled Eigen headers), Eigen Library Authors [cph] (Copyright holders of bundled Eigen headers in src/include/eigen3) |
| Maintainer | Tuo Zhao <tourzhao@gatech.edu> |
| License | GPL-3 |
| Version | 1.5 |
| Package repository | View on CRAN |
| Installation |
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