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) <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

Documentation:

Reference manual:	PRIMAL.pdf
Vignettes:	vignette

Downloads:

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

Linking:

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