Computational codes for a class of Bayesian finite mixture quantile regression models for bounded responses in R using NIMBLE

Version 1.1
Galindo, Áurea Fonseca Lopes; Azevedo, Caio Lucidius Naberezny, 2026, "Computational codes for a class of Bayesian finite mixture quantile regression models for bounded responses in R using NIMBLE", https://doi.org/10.25824/redu/WB8YHO, Repositório de Dados de Pesquisa da Unicamp , V1
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Description
Estes dados são referentes a rotinas em R que implementam uma classe de modelos de regressão quantílica baseados em misturas finitas para respostas limitadas, desenvolvidos sob uma abordagem Bayesiana utilizando o pacote NIMBLE. Os códigos implementam computacionalmente os métodos propostos na tese intitulada “Finite Mixture–Based Quantile Regression Models for Bounded Responses”, incluindo os procedimentos de estimação, seleção de modelos e análise de diagnóstico derivados da formulação teórica apresentada no trabalho. As implementações correspondem às extensões da classe de modelos desenvolvida na tese, abrangendo desde misturas com duas subpopulações até misturas com K componentes, bem como versões com efeitos aleatórios, mantendo o foco em um contexto Bayesiano. Os códigos estão organizados em três repositórios públicos: https://github.com/aureaflg/mixUGG.git https://github.com/aureaflg/FmixUGG.git https://github.com/aureaflg/mixed_FmixUGG.git
Subject Mathematical Sciences
Keyword Computational codes, Computational codes, Proportion data, Finite mixture model
Related Publication GALINDO, Áurea Fonseca Lopes. Finite Mixture–Based Quantile Regression Models for Bounded Responses. 2025. 1 recurso online (105 p.) Tese (doutorado) - Universidade Estadual de Campinas Instituto de Matemática Estatística e Computação Científica, Campinas, SP.
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