Introduction | The DBR Mathematical Framework | Using the DBR Package | Discussion | Setup
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:
DBR_1.4.1.tar.gz
DBR_1.4.1.zip(r-4.7-any)DBR_1.4.1.zip(r-4.6-any)DBR_1.4.1.zip(r-4.5-any)
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✨
DESCRIPTION
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:
- pain - Pain Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:457780458f. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 113 | ||
| source / vignettes | OK | 156 | ||
| linux-release-x86_64 | OK | 113 | ||
| macos-release-arm64 | OK | 111 | ||
| macos-oldrel-arm64 | OK | 79 | ||
| windows-devel | OK | 80 | ||
| windows-release | OK | 88 | ||
| windows-oldrel | OK | 77 | ||
| wasm-release | OK | 83 |
Exports:coda_wrapperdbrdbr.control
Dependencies:arscodadlmlatticeMfUSampler
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Utility function to call MCMC diagnostic functions in the coda package | coda_wrapper |
| Discretised Beta Regression for Survey-Response Analysis | dbr dbr.control |
| Pain Data | pain |
| Predict method for Discretised Beta Regression Fits | predict.dbr |
| Summary, print, plot and coef methods for Discretised Beta Regression Fits | coef.dbr plot.dbr print.dbr summary.dbr |
