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]

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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.

3 exports 0.00 score 5 dependencies 11 scripts 398 downloads

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

TargetResultDate
Doc / VignettesOKAug 11 2024
R-4.5-winOKAug 11 2024
R-4.5-linuxOKAug 11 2024
R-4.4-winOKAug 11 2024
R-4.4-macOKAug 11 2024
R-4.3-winOKAug 11 2024
R-4.3-macOKAug 11 2024

Exports:coda_wrapperdbrdbr.control

Dependencies:arscodadlmlatticeMfUSampler

Bayesian Discretised Beta Regression

Rendered fromDBR.Rnwusingutils::Sweaveon Aug 11 2024.

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