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Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.
| Version: | 1.0.2 |
| Imports: | Matrix |
| LinkingTo: | Rcpp, RcppEigen |
| Published: | 2020-01-22 |
| DOI: | 10.32614/CRAN.package.PRIMAL |
| Author: | Zichong Li, Qianli Shen |
| Maintainer: | Zichong Li <zichongli5 at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | PRIMAL results |
| Reference manual: | PRIMAL.pdf |
| Vignettes: |
vignette |
| Package source: | PRIMAL_1.0.2.tar.gz |
| Windows binaries: | r-devel: PRIMAL_1.0.2.zip, r-release: PRIMAL_1.0.2.zip, r-oldrel: PRIMAL_1.0.2.zip |
| macOS binaries: | r-release (arm64): PRIMAL_1.0.2.tgz, r-oldrel (arm64): PRIMAL_1.0.2.tgz, r-release (x86_64): PRIMAL_1.0.2.tgz, r-oldrel (x86_64): PRIMAL_1.0.2.tgz |
| Old sources: | PRIMAL archive |
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