<?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>Lorenz and Penalized Lorenz Regressions</dc:title>
  <dc:title>R package LorenzRegression version 2.3.1</dc:title>
  <dc:description>Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) &lt;doi:10.1016/j.csda.2021.107347&gt; and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) &lt;doi:10.1214/23-EJS2200&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.3.1)</dc:relation>
  <dc:relation>Imports: stats, ggplot2, parsnip, boot, rsample, parallel, doParallel,
foreach, MASS, GA, Rearrangement, progress, Rcpp (&gt;= 0.11.0)</dc:relation>
  <dc:relation>LinkingTo: Rcpp, RcppArmadillo</dc:relation>
  <dc:relation>Suggests: rmarkdown</dc:relation>
  <dc:creator>Alexandre Jacquemain &lt;aljacquemain@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Alexandre Jacquemain [aut, cre] (ORCID:
    &lt;https://orcid.org/0000-0001-9349-780X&gt;),
  Xingjie Shi [ctb] (Author of an R implementation of the FABS algorithm
    available at https://github.com/shuanggema/Fabs, of which function
    Lorenz.FABS is derived)</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2026-02-12</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=LorenzRegression</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.LorenzRegression</dc:identifier>
</oai_dc:dc>
