<?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>Low-Rank Decomposition of Brain Connectivity Matrices with
Uniform Sparsity</dc:title>
  <dc:title>R package LOCUS version 1.0</dc:title>
  <dc:description>To decompose symmetric matrices such as brain connectivity matrices so that one can extract sparse latent component matrices and also estimate mixing coefficients, a blind source separation (BSS) method named LOCUS was proposed in Wang and Guo (2023) &lt;arXiv:2008.08915&gt;. For brain connectivity matrices, the outputs correspond to sparse latent connectivity traits and individual-level trait loadings. </dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.1.0), ica, MASS, far</dc:relation>
  <dc:creator>Jialu Ran &lt;jialuran422@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Yikai Wang [aut, cph],
  Jialu Ran [aut, cre],
  Ying Guo [aut, ths]</dc:contributor>
  <dc:rights>GPL-2</dc:rights>
  <dc:date>2022-10-04</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=LOCUS</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.LOCUS</dc:identifier>
</oai_dc:dc>