<?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>Weighted, Two-Mode, and Longitudinal Networks Analysis</dc:title>
  <dc:title>R package tnet version 3.0.16</dc:title>
  <dc:subject>CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference)</dc:subject>
  <dc:subject>CRAN Task View: NetworkAnalysis (https://CRAN.R-project.org/view=NetworkAnalysis)</dc:subject>
  <dc:description>Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 2.13.0), igraph, survival</dc:relation>
  <dc:creator>Tore Opsahl &lt;tore@opsahl.co.uk&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Tore Opsahl</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2020-02-24</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=tnet</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.tnet</dc:identifier>
</oai_dc:dc>