DBR: Discrete Beta Regression

Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses. Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler (Neal (2003) <doi:10.1214/aos/1056562461>), as implemented in the R package MfUSampler (Mahani and Sharabiani (2017) <doi:10.18637/jss.v078.c01>).

Version: 1.4.1
Depends: R (≥ 3.5.0)
Imports: MfUSampler, methods, coda
Published: 2023-02-20
DOI: 10.32614/CRAN.package.DBR
Author: Alireza Mahani [cre, aut], Mansour Sharabiani [aut], Alex Bottle [aut], Cathy Price [aut]
Maintainer: Alireza Mahani <alireza.s.mahani at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: DBR results


Reference manual: DBR.pdf
Vignettes: Bayesian Discretised Beta Regression


Package source: DBR_1.4.1.tar.gz
Windows binaries: r-devel: DBR_1.4.1.zip, r-release: DBR_1.4.1.zip, r-oldrel: DBR_1.4.1.zip
macOS binaries: r-release (arm64): DBR_1.4.1.tgz, r-oldrel (arm64): DBR_1.4.1.tgz, r-release (x86_64): DBR_1.4.1.tgz, r-oldrel (x86_64): DBR_1.4.1.tgz
Old sources: DBR archive


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