cpd: An R Package for Complex Pearson Distributions

María José Olmo-Jiménez (), Silverio Vílchez-López and José Rodríguez-Avi
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María José Olmo-Jiménez: Department of Statistics and Operations Research, University of Jaén, 23071 Jaén, Spain
Silverio Vílchez-López: Department of Statistics and Operations Research, University of Jaén, 23071 Jaén, Spain
José Rodríguez-Avi: Department of Statistics and Operations Research, University of Jaén, 23071 Jaén, Spain

Mathematics, 2022, vol. 10, issue 21, 1-15

Abstract: The complex Pearson (CP) distributions are a family of probability models for count data generated by the Gaussian hypergeometric function with complex arguments. The complex triparametric Pearson (CTP) distribution and its biparametric versions, the complex biparametric Pearson (CBP) and the extended biparametric Waring (EBW) distributions, belong to this family. They all have explicit expressions of the probability mass function (pmf), probability generating function and moments, so they are easy to handle from a computational point of view. Moreover, the CTP and EBW distributions can model over- and underdispersed count data, whereas the CBP can only handle overdispersed data, but unlike other well-known overdispersed distributions, the overdispersion is not due to an excess of zeros but other low values of the variable. Finally, the EBW distribution allows the variance to be split into three uniquely identifiable components: randomness, liability and proneness. These properties make the CP distributions of interest in the modeling of a great variety of data. For this reason, and for trying to spread their use, we have implemented an R package called cpd that contains the pmf, distribution function, quantile function and random generation for these distributions. In addition, the package contains fitting functions according to the maximum likelihood. This package is available from the Comprehensive R Archive Network (CRAN). In this work, we describe all the functions included in the cpd package, and we illustrate their usage with several examples. Moreover, the release of a plugin in order to use the package from the interface R Commander tries to contribute to the spreading of these models among non-advanced users.

Keywords: count data models; overdispersion; underdispersion; R package (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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