Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Justine Lequesne, Philippe Regnault
Title: vsgoftest: An R Package for Goodness-of-Fit Testing Based on Kullback-Leibler Divergence
Abstract: The R package vsgoftest performs goodness-of-fit (GOF) tests, based on Shannon entropy and Kullback-Leibler divergence, developed by Vasicek (1976) and Song (2002), of various classical families of distributions. The so-called Vasicek-Song (VS) tests are intended to be applied to continuous data - typically drawn from a density distribution, even including ties. Their excellent properties - they exhibit high power in a large variety of situations, make them relevant alternatives to classical GOF tests in any domain of application requiring statistical processing. The theoretical framework of VS tests is summarized and followed by a detailed description of the different features of the package. The power and computational time performances of VS tests are studied through their comparison with other GOF tests. Application to real datasets illustrates the easy-to-use functionalities of the vsgoftest package.

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Paper: vsgoftest: An R Package for Goodness-of-Fit Testing Based on Kullback-Leibler Divergence     Download PDF (Downloads: 317)
Supplements:
vsgoftest_1.0-1.tar.gz: R source package Download (Downloads: 25; 152KB)
v96c01.R: R replication code Download (Downloads: 32; 11KB)

DOI: 10.18637/jss.v096.c01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.