| vsgoftest-package {vsgoftest} | R Documentation |
Goodness-of-Fit Tests Based on Kullback-Leibler Divergence
Description
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>.
Details
The DESCRIPTION file:
| Package: | vsgoftest |
| Type: | Package |
| Title: | Goodness-of-Fit Tests Based on Kullback-Leibler Divergence |
| Version: | 1.0-1 |
| Date: | 2020-12-17 |
| Author: | Justine Lequesne [aut], Philippe Regnault [aut, cre] |
| Maintainer: | Philippe Regnault <philipperegnault@hotmail.com> |
| Description: | 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>. |
| Depends: | stats, fitdistrplus |
| Imports: | Rcpp (>= 0.12.1) |
| Suggests: | knitr |
| VignetteBuilder: | knitr |
| LinkingTo: | Rcpp |
| Encoding: | UTF-8 |
| License: | GPL (>=2) |
| Packaged: | 2020-12-17 12:12:51 UTC; philippe |
Index of help topics:
contaminants Organic and inorganic contaminant concentration
data
dlaplace The Laplace distribution
dpareto The Pareto distribution
entropy.estimate Vasicek estimate of differential Shannon
Entropy
vs.test Vasicek-Song goodness-of-fit test for various
distributions
vsgoftest-package Goodness-of-Fit Tests Based on Kullback-Leibler
Divergence
Further information is available in the following vignettes:
vsgoftest_tutorial | Tutorial (source, pdf) |
Author(s)
Justine Lequesne [aut], Philippe Regnault [aut, cre]
Maintainer: Philippe Regnault <philipperegnault@hotmail.com>
References
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
Examples
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