PRIMAL: Parametric Simplex Method for Sparse Learning

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) <>.

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>
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:, r-release:, r-oldrel:
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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