bayesbr-package: bayesbr: A package for Bayesian Beta Regression
In bayesbr: Beta Regression on a Bayesian Model
Description
The package fits or the beta regression model under the view of Bayesian statistics using the No-U-Turn-Sampler (NUTS) method for computational calculations.The model can be adjusted considering or not the spatial effect on the parameters. In addition to showing the coefficients, the package also has functions for displaying residuals, checking the model's convergence, checking the quality of the model and other utilities that may be useful.
References
arXiv:1111.4246 Hoffman, M. D., and Gelman, A. (2014). The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593-1623.
doi: 10.1080/0266476042000214501 Ferrari, S.L.P., and Cribari-Neto, F. (2004).
Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.
bayesbr documentation built on July 17, 2021, 1:07 a.m.
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Description
The package fits or the beta regression model under the view of Bayesian statistics using the No-U-Turn-Sampler (NUTS) method for computational calculations.The model can be adjusted considering or not the spatial effect on the parameters. In addition to showing the coefficients, the package also has functions for displaying residuals, checking the model's convergence, checking the quality of the model and other utilities that may be useful.
References
arXiv:1111.4246 Hoffman, M. D., and Gelman, A. (2014). The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593-1623.
doi: 10.1080/0266476042000214501 Ferrari, S.L.P., and Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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