While the Poisson distribution is a classical statistical model for
count data, the distributional model hinges on the constraining property
that its mean equal its variance. This text instead introduces the
Conway-Maxwell-Poisson distribution and motivates its use in developing
flexible statistical methods based on its distributional form. This
two-parameter model not only contains the Poisson distribution as a
special case but, in its ability to account for data over- or
under-dispersion, encompasses both the geometric and Bernoulli
distributions. The resulting statistical methods serve in a multitude of
ways, from an exploratory data analysis tool, to a flexible modeling
impetus for varied statistical methods involving count data. The first
comprehensive reference on the subject, this text contains numerous
illustrative examples demonstrating R code and output. It is essential
reading for academics in statistics and data science, as well as
quantitative researchers and data analysts in economics, biostatistics
and other applied disciplines.