Please use this identifier to cite or link to this item: https://hdl.handle.net/10953/1931
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dc.contributor.authorSáez-Castillo, Antonio J.-
dc.contributor.authorConde-Sánchez, Antonio-
dc.contributor.authorMartínez, Francisco-
dc.date.accessioned2024-02-02T13:36:02Z-
dc.date.available2024-02-02T13:36:02Z-
dc.date.issued2022-12-
dc.identifier.issn2054-5703es_ES
dc.identifier.other10.1098/RSOS.201756es_ES
dc.identifier.urihttps://doi.org/10.32614/RJ-2023-002es_ES
dc.identifier.urihttps://hdl.handle.net/10953/1931-
dc.description.abstractIn 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.es_ES
dc.description.sponsorshipThis work was supported by MCIN/AEI/10.13039/501100011033 [project PID2019-107793GB-I00].es_ES
dc.language.isoenges_ES
dc.publisherThe R Foundationes_ES
dc.relation.ispartofR Journal [2022]; [14/4]: [121-140]es_ES
dc.subjecthyper-Poisson regressiones_ES
dc.subjectCOM-Poisson regressiones_ES
dc.subjectCount dataes_ES
dc.subjectSoftwarees_ES
dc.titleDGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Frameworkes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
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