Package: DBR 1.4.1

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>).

Authors:Alireza Mahani [cre, aut], Mansour Sharabiani [aut], Alex Bottle [aut], Cathy Price [aut]

DBR_1.4.1.tar.gz
DBR_1.4.1.zip(r-4.7)DBR_1.4.1.zip(r-4.6)DBR_1.4.1.zip(r-4.5)
DBR_1.4.1.tgz(r-4.6-any)DBR_1.4.1.tgz(r-4.5-any)
DBR_1.4.1.tar.gz(r-4.7-any)DBR_1.4.1.tar.gz(r-4.6-any)
DBR_1.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
DBR/json (API)

# Install 'DBR' in R:
install.packages('DBR', repos = c('https://asmahani.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.11 score 13 scripts 238 downloads 3 exports 5 dependencies

Last updated from:457780458f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK113
source / vignettesOK181
linux-release-x86_64OK130
macos-release-arm64OK102
macos-oldrel-arm64OK73
windows-develOK90
windows-releaseOK111
windows-oldrelOK80
wasm-releaseOK85

Exports:coda_wrapperdbrdbr.control

Dependencies:arscodadlmlatticeMfUSampler

Bayesian Discretised Beta Regression

Rendered fromDBR.Rnwusingutils::Sweaveon May 12 2026.

Last update: 2022-08-06
Started: 2022-03-23