<?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>Robust Re-Scaling to Better Recover Latent Effects in Data</dc:title>
  <dc:title>R package rrscale version 1.0</dc:title>
  <dc:description>Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser &amp; Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, &lt;doi:10.1080/10618600.2020.1741379&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 3.5.0)</dc:relation>
  <dc:relation>Imports: DEoptim, nloptr, abind</dc:relation>
  <dc:relation>Suggests: knitr, rmarkdown, testthat, ggplot2, reshape2</dc:relation>
  <dc:creator>Gregory Hunt &lt;ghunt@wm.edu&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Gregory Hunt [aut, cre],
  Johann Gagnon-Bartsch [aut]</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2020-05-26</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=rrscale</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.rrscale</dc:identifier>
</oai_dc:dc>