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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) <http://asq.org/qic/display-item/index.html?item=11407>. 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.
Version: | 0.2.0 |
Imports: | ismev, rmutil |
Published: | 2018-06-07 |
DOI: | 10.32614/CRAN.package.gnFit |
Author: | Ali Saeb |
Maintainer: | Ali Saeb <ali.saeb at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL] |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | gnFit results |
Reference manual: | gnFit.pdf |
Package source: | gnFit_0.2.0.tar.gz |
Windows binaries: | r-devel: gnFit_0.2.0.zip, r-release: gnFit_0.2.0.zip, r-oldrel: gnFit_0.2.0.zip |
macOS binaries: | r-release (arm64): gnFit_0.2.0.tgz, r-oldrel (arm64): gnFit_0.2.0.tgz, r-release (x86_64): gnFit_0.2.0.tgz, r-oldrel (x86_64): gnFit_0.2.0.tgz |
Old sources: | gnFit archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.