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Understanding why a random variable is actually random has been in the core of Statistics from its beginnings. The generalized Waring regression model for count data explains that inherent variability is given by three possible sources: randomness, liability and proneness. The model extends the negative binomial regression model and it is not included in the family of generalized linear models. In order to avoid that shortcoming, we developed the R package for fitting, describing and validating the model. The version we introduce in this communication provides a new design of the modelling function as well as new methods operating on the associated fitted model objects, so that the new software integrates easily into the computational toolbox for modelling count data in R. The release of a plug-in in order to use the package from the interface R Commander tries to contribute to the spreading of the model among non-advanced users. We illustrate the usage and the possibilities of the software with two examples from the fields of health and sport. [ABSTRACT FROM AUTHOR]

In this paper we present a family of Pearson's discrete distributions which are generated by the hypergeometric function p+1Fp, an univariate extension of the Gaussian hypergeometric function. It allows us to generalize the study of this type of distributions, whatever the order of the hypergeometric function which is considered. We study the properties that they present, like a recurrence relation that the moments verify, from which we can use the moment's method to estimate the parameters. Also we have obtained a summation result through which we can calculate the probability mass function and the moments of a wide class of distributions. [ABSTRACT FROM AUTHOR]