DGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Framework

In recent years the use of regression models for under-dispersed count data, such as COM-Poisson or hyper-Poisson models, has increased. In this paper the DGLMExtPois package is presented. DGLMExtPois includes a new procedure to estimate the coefficients of a hyper-Poisson regression model within a GLM framework. The estimation process uses a gradient-based algorithm to solve a nonlinear constrained optimization problem. The package also provides an implementation of the COM-Poisson model, proposed by Huang (2017), to make it easy to compare both models. The functionality of the package is illustrated by fitting a model to a real dataset. Furthermore, an experimental comparison is made with other related packages, although none of these packages allow you to fit a hyper-Poisson model.

Antonio J. Sáez-Castillo (University of Jaén) , Antonio Conde-Sánchez (University of Jaén) , Francisco Martínez (University of Jaén)
2023-02-10

Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-002.zip

A. Huang. Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts. Statistical Modelling, 17(6): 359–380, 2017. URL https://doi.org/10.1177/1471082X17697749.

References

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Citation

For attribution, please cite this work as

Sáez-Castillo, et al., "DGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Framework", The R Journal, 2023

BibTeX citation

@article{RJ-2023-002,
  author = {Sáez-Castillo, Antonio J. and Conde-Sánchez, Antonio and Martínez, Francisco},
  title = {DGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Framework},
  journal = {The R Journal},
  year = {2023},
  note = {https://doi.org/10.32614/RJ-2023-002},
  doi = {10.32614/RJ-2023-002},
  volume = {14},
  issue = {4},
  issn = {2073-4859},
  pages = {121-140}
}