<?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>Multiple-Scaled Clustering</dc:title>
  <dc:title>R package MSclust version 1.0.4</dc:title>
  <dc:subject>CRAN Task View: Cluster (https://CRAN.R-project.org/view=Cluster)</dc:subject>
  <dc:description>Model based clustering using 
    the multivariate multiple Scaled t (MST) and multivariate multiple 
    scaled contaminated normal (MSCN) distributions. The MST is an 
    extension of the multivariate Student-t distribution to include 
    flexible tail behaviors, Forbes, F. &amp; Wraith, D. (2014) &lt;doi:10.1007/s11222-013-9414-4&gt;. The MSCN represents a  heavy-tailed
    generalization of the multivariate normal (MN) distribution to
    model elliptical contoured scatters in the presence of mild outliers
    (also referred to as "bad" points) and automatically detect bad points, Punzo, A. &amp; Tortora, C. (2021) &lt;doi:10.1177/1471082X19890935&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.5)</dc:relation>
  <dc:relation>Imports: gtools, Matrix, mclust, mnormt, mvtnorm, psych, cluster,
ggplot2, GGally</dc:relation>
  <dc:creator>Cristina Tortora &lt;grikris1@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Cristina Tortora [aut, cre, cph] (ORCID:
    &lt;https://orcid.org/0000-0001-8351-3730&gt;),
  Antonio Punzo [aut] (ORCID: &lt;https://orcid.org/0000-0001-7742-1821&gt;),
  Louis Tran [aut]</dc:contributor>
  <dc:rights>GPL (&gt;= 2)</dc:rights>
  <dc:date>2024-04-22</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=MSclust</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.MSclust</dc:identifier>
</oai_dc:dc>
