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: 1.0.0
Imports: Matrix
LinkingTo: Rcpp, RcppEigen
Published: 2019-10-02
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

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Reference manual: PRIMAL.pdf
Vignettes: vignette
Package source: PRIMAL_1.0.0.tar.gz
Windows binaries: r-devel: PRIMAL_1.0.0.zip, r-devel-gcc8: PRIMAL_1.0.0.zip, r-release: PRIMAL_1.0.0.zip, r-oldrel: PRIMAL_1.0.0.zip
OS X binaries: r-release: PRIMAL_1.0.0.tgz, r-oldrel: PRIMAL_1.0.0.tgz

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