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  "Title": "Multiscale Geographically Weighted Negative Binomial Regression",
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  "Authors@R": "c(\nperson(\"Juliana\", \"Rosa\", , \"julianamrosa00@gmail.com\", role=c(\"aut\", \"cre\")),\nperson(\"Jéssica\", \"Vasconcelos\", , \"jehh.vasconcelosabreu@gmail.com\", role=\"aut\"),\nperson(\"Alan\", \"da Silva\", , \"alansilva@unb.br\", role=\"aut\"))",
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  "Description": "Fits a geographically weighted regression model with\ndifferent scales for each covariate. Uses the negative binomial\ndistribution as default, but also accepts the normal, Poisson,\nor logistic distributions. Can fit the global versions of each\nregression and also the geographically weighted alternatives\nwith only one scale, since they are all particular cases of the\nmultiscale approach. Hanchen Yu (2024). \"Exploring Multiscale\nGeographically Weighted Negative Binomial Regression\", Annals\nof the American Association of Geographers\n<doi:10.1080/24694452.2023.2289986>. Fotheringham AS, Yang W,\nKang W (2017). \"Multiscale Geographically Weighted Regression\n(MGWR)\", Annals of the American Association of Geographers\n<doi:10.1080/24694452.2017.1352480>. Da Silva AR, Rodrigues TCV\n(2014). \"Geographically Weighted Negative Binomial Regression -\nincorporating overdispersion\", Statistics and Computing\n<doi:10.1007/s11222-013-9401-9>.",
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