<?xml version="1.0" encoding="UTF-8"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Parametric Simplex Method for Sparse Learning</dc:title>
  <dc:title>R package PRIMAL version 1.0.3</dc:title>
  <dc:description>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) &lt;https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: Matrix</dc:relation>
  <dc:relation>LinkingTo: Rcpp, RcppEigen</dc:relation>
  <dc:creator>Zichong Li &lt;zichongli5@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Zichong Li [aut, cre],
  Qianli Shen [aut]</dc:contributor>
  <dc:rights>GPL (&gt;= 2)</dc:rights>
  <dc:date>2025-12-03</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=PRIMAL</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.PRIMAL</dc:identifier>
</oai_dc:dc>