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<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>Goodness of Fit Test for Continuous Distribution Functions</dc:title>
  <dc:title>R package gnFit version 0.2.0</dc:title>
  <dc:description>Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) &lt;http://asq.org/qic/display-item/index.html?item=11407&gt;. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: ismev, rmutil</dc:relation>
  <dc:creator>Ali Saeb &lt;ali.saeb@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Ali Saeb</dc:contributor>
  <dc:rights>GPL</dc:rights>
  <dc:date>2018-06-07</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=gnFit</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.gnFit</dc:identifier>
</oai_dc:dc>