The Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the Conway-Maxwell-Poisson (CMP) distribution -a two-parameter generalization of the Poisson distribution that can accommodate data over-or under-dispersion. This work further generalizes the ideas of the CMP distribution by considering sums of CMP random variables to establish a flexible class of distributions that encompasses the Poisson, negative binomial, and binomial distributions as special cases. This sum-of-Conway-Maxwell-Poissons (sCMP) class captures the CMP and its special cases, as well as the classical negative binomial and binomial distributions. Through simulated and real data examples, we demonstrate this model's flexibility, encompassing several classical distributions as well as other count data distributions containing significant data dispersion.

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Following publication of the original article (Sellers et al., 2017), the authors reported that the typesetters had misinterpreted some of the edits included in their proof corrections, namely instances of "sp" to denote that an extra space was required. The original article has been corrected.