Flexible Regression Models for Count Data Based on Renewal Processes: The Countr Package

Georgi Boshnakov, Tarak Kharrat, Ian Mchale, Rose Baker

    Research output: Contribution to journalArticlepeer-review


    A new alternative to the standard Poisson regression model for count data is
    suggested. This new family of models is based on discrete distributions
    derived from renewal processes, i.e. distributions of the number of events by
    some time t. Unlike the Poisson model, these models have, in general,
    time-dependent hazard functions. Any survival distribution can be used to
    describe the inter-arrival times between events, which gives a rich class of
    count processes with great flexibility for modelling both underdispersed and
    overdispersed data. The R package Countr provides a function, renewalCount(),
    for fitting renewal count regression models and methods for working with the
    fitted models. The interface is designed to mimic the glm() interface
    and standard methods for model exploration, diagnosis and prediction are
    implemented. Package Countr implements state-of-the-art recently
    developed methods for fast computation of the count probabilities.
    The package functionalities are illustrated using several datasets.
    Original languageEnglish
    Pages (from-to)0
    JournalJournal of Statistical Software
    Issue number13
    Publication statusPublished - 21 Aug 2019


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