Bayesian Beta Regression

Provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2021, "Bayesian beta regression for bounded responses with unknown supports") < https://sites.google.com/view/haimingzhou/research>.


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install.packages("betaBayes")

1.0 by Haiming Zhou, 3 months ago


Browse source code at https://github.com/cran/betaBayes


Authors: Haiming Zhou [aut, cre, cph] , Xianzheng Huang [aut]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp, splines, methods, coda, betareg

Linking to Rcpp, RcppArmadillo


See at CRAN