Beta Regression in R. Cribari-Neto, F. Paper abstract bibtex This introduction to the R package betareg is a (slightly) modified version of Cribari-Neto and Zeileis (2010), published in the Journal of Statistical Software. A follow-up paper with various extensions is Grün, Kosmidis, and Zeileis (2012) – a slightly modified version of which is also provided within the package as vignette("betareg-ext", package = "betareg") The class of beta regression models is commonly used by practitioners to model vari-ables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (poten-tially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises.

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abstract = {This introduction to the R package betareg is a (slightly) modified version of Cribari-Neto and Zeileis (2010), published in the Journal of Statistical Software. A follow-up paper with various extensions is Grün, Kosmidis, and Zeileis (2012) – a slightly modified version of which is also provided within the package as vignette("betareg-ext", package = "betareg") The class of beta regression models is commonly used by practitioners to model vari-ables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (poten-tially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises.},
bibtype = {article},
author = {Cribari-Neto, Francisco}
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