vsgoftest-package: Goodness-of-Fit Tests Based on Kullback-Leibler Divergence
In vsgoftest: Goodness-of-Fit Tests Based on Kullback-Leibler Divergence

Description Details Author(s) References Examples

An implementation of Vasicek and Song goodness-of-fit tests. Several functions are provided to estimate differential Shannon entropy, i.e., estimate Shannon entropy of real random variables with density, and test the goodness-of-fit of some family of distributions, including uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace and beta distributions; see Lequesne and Regnault (2020) <doi:10.18637/jss.v096.c01>.

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Justine Lequesne [aut], Philippe Regnault [aut, cre]

Maintainer: Philippe Regnault <philipperegnault@hotmail.com>

Vasicek, O., A test for normality based on sample entropy, Journal of the Royal Statistical Society, 38(1), 54-59 (1976).

Song, K. S., Goodness-of-fit tests based on Kullback-Leibler discrimination information, Information Theory, IEEE Transactions on, 48(5), 1103-1117 (2002).

Girardin, V., Lequesne, J. Entropy-based goodness-of-fit tests - a unifying framework. Application to DNA replication. Communications in Statistics: Theory and Methods (2017). https://doi.org/10.1080/03610926.2017.1401084

Lequesne, J., Regnault, P. vsgoftest: An R Package for Goodness-of-Fit Testing Based on Kullback-Leibler Divergence. Journal of Statistical Software, 96 (2020). doi:10.18637/jss.v096.c01

set.seed(1)
samp <- rnorm(50, mean = 2, s = 3)

##Estimating entropy
entropy.estimate(x = samp, window = 8)
log(2*pi*exp(1))/2 #true value of entropy of normal distribution

##Testing normality
vs.test(x = samp, densfun = 'dnorm', param = c(2,3), B = 500) #Simple null hypothesis
vs.test(x = samp, densfun='dnorm', B = 500) #Composite null hypothesis