Morales, Fidel Ernesto CastroCarneiro, Thiago Mota2019-12-202021-09-202019-12-202021-09-202019CARNEIRO, Thiago Mota. Pacote GeoPoisson: implementação e aplicações. 2019. 44f. Trabalho de Conclusão de Curso (Graduação em Estatística) - Departamento de Estatística, Universidade Federal do Rio Grande do Norte, Natal, 2019.https://repositorio.ufrn.br/handle/123456789/34280The application of geostatistical models have been growing rapidly in the last decades. Geostatistical models for counting via Poisson process have been a versatile tool for environ- mental sciences in predicting anomalous events (excessive rains, lethal CO concentration). However, the counting via homogeneous Poisson process model is applicable to a very narrow scope of phenomena. For thus a geostatistical model was developed for counting via non homogeneous Poisson process. We propose a new package in R software with a set of functions to estimate the model above. The usage of the package is exemplified with data obtained from 29 measuring stations in Piaui and Maranhao - Brazil, from 1980 to 2010 published by ANA (Agencia Nacional de Aguas - Brazilian National Water Agency). The main function obtains credibility intervals to the parameters of the model in a Bayesian approach through MCMC, specifically the Metropolis-Hastings algorithm within Gibbs sampling. In addition, we made a function which returns an interpolation map of anomalous events in neighboring unobserved regions.Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/GeoestatísticaPoisson Não HomogêneoMCMCGeostatisticsNon-Homogeneous PoissonbachelorThesisEstatística Aplicada