good : An R package for modelling count data

Abstract

Organisms-related data often appear as counts. The Poisson distribution is the most popular choice for modelling count data, but this distribution assumes equidispersion, which is usually not satisfied in real-world data. Deviations from the Poisson assumption lead to discrete-valued distributions that can fit over- and/or underdispersion. Although models for count data with over-dispersion have been widely considered in the literature, models for underdispersion-the opposite phenomenon-have received less attention because underdispersion is relatively common only in certain research fields, including ecology. The Good distribution is a flexible option for modelling count data with over-dispersion or underdispersion, although no R packages are available so far offering functionalities such as calculating quantiles, probabilities, etc., of a Good distribution or providing a method for modelling a Good-distributed output based on a number of potential predictors. This paper presents the R package good, which computes the standard probabilistic functions, generates random samples from a population following a Good distribution and estimates the Good regression.