Mean and Variance Modeling of Under- and Overdispersed Count Data

Abstract

This article describes the R package CountsEPPM and its use in determining maximum likelihood estimates of the parameters of extended Poisson process models. These provide a Poisson process based family of flexible models that can handle both underdispersion and overdispersion in observed count data, with the negative binomial and Poisson distributions being special cases. Within CountsEPPM models with mean and variance related to covariates are constructed to match a generalized linear model formulation. Use of the package is illustrated by application to several published datasets.