<?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>Mapping ML Scores to Calibrated Predictions</dc:title>
  <dc:title>R package CalibratR version 0.1.2</dc:title>
  <dc:description>Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Depends: R (&gt;= 2.10.0)</dc:relation>
  <dc:relation>Imports: ggplot2, pROC, reshape2, parallel, foreach, stats,
fitdistrplus, doParallel</dc:relation>
  <dc:creator>Dominik Heider &lt;heiderd@mathematik.uni-marburg.de&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Johanna Schwarz, Dominik Heider</dc:contributor>
  <dc:rights>LGPL-3</dc:rights>
  <dc:date>2019-08-19</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=CalibratR</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.CalibratR</dc:identifier>
</oai_dc:dc>