betaBayes: 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>.

Version: 1.0
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 0.11.1), splines, methods, coda, betareg
LinkingTo: Rcpp, RcppArmadillo (≥ 0.4.300.0)
Published: 2021-06-08
Author: Haiming Zhou [aut, cre, cph], Xianzheng Huang [aut]
Maintainer: Haiming Zhou <zhouh at niu.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Citation: betaBayes citation info
Materials: README
CRAN checks: betaBayes results

Downloads:

Reference manual: betaBayes.pdf
Package source: betaBayes_1.0.tar.gz
Windows binaries: r-devel: betaBayes_1.0.zip, r-devel-UCRT: betaBayes_1.0.zip, r-release: betaBayes_1.0.zip, r-oldrel: betaBayes_1.0.zip
macOS binaries: r-release (arm64): betaBayes_1.0.tgz, r-release (x86_64): betaBayes_1.0.tgz, r-oldrel: betaBayes_1.0.tgz

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