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-4.5)DBR_1.4.1.zip(r-4.4)DBR_1.4.1.zip(r-4.3)
DBR_1.4.1.tgz(r-4.4-any)DBR_1.4.1.tgz(r-4.3-any)
DBR_1.4.1.tar.gz(r-4.5-noble)DBR_1.4.1.tar.gz(r-4.4-noble)
DBR_1.4.1.tgz(r-4.4-emscripten)DBR_1.4.1.tgz(r-4.3-emscripten)
DBR.pdf |DBR.html✨
DBR/json (API)

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

Peer review:

Datasets:

On CRAN:

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

2.04 score 11 scripts 241 downloads 3 exports 5 dependencies

Last updated 2 years agofrom:457780458f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 09 2024
R-4.5-winOKNov 09 2024
R-4.5-linuxOKNov 09 2024
R-4.4-winOKNov 09 2024
R-4.4-macOKNov 09 2024
R-4.3-winOKNov 09 2024
R-4.3-macOKNov 09 2024

Exports:coda_wrapperdbrdbr.control

Dependencies:arscodadlmlatticeMfUSampler

Bayesian Discretised Beta Regression

Rendered fromDBR.Rnwusingutils::Sweaveon Nov 09 2024.

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