vsgoftest: An Package for Goodness-of-Fit Testing Based on Kullback-Leibler Divergence

TL;DR

The vsgoftest R-package provides goodness-of-fit tests based on Shannon entropy and Kullback-Leibler divergence, enabling users to evaluate how well data fit classical distributions efficiently and easily.

Contribution

It introduces the Vasicek-Song tests framework into an R package, with detailed features, performance analysis, and practical applications for distribution fitting.

Findings

VS tests have competitive power compared to other GOF tests.

The package demonstrates efficient computation times.

Applications show ease of use and practical relevance.

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 theoretical framework of the so-called Vasicek-Song (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.