R-fitODBOD
The facts below are what the maker publishes about R-fitODBOD, checked and dated. Our editors have not written about it yet.
The facts
Each one read from R-fitODBOD's own pages, with the date it was checked.
What it does
- Data preparation
- The BODextract function can prepare raw binomial outcome data in the frequency format used in the examples.checked 12 Oct 2026
- Dependencies
- The beta-binomial example uses mle2 from the bbmle package for maximum-likelihood estimation.checked 12 Oct 2026
- Distribution families
- The package lists six alternate binomial distributions and eight binomial mixture distributions.checked 12 Oct 2026
- Estimation
- The package documentation describes maximum likelihood and method of moments approaches for estimating distribution parameters.checked 12 Oct 2026
- Example data
- The site demonstrates the workflow with alcohol consumption data from 399 respondents in the Netherlands in 1983.checked 12 Oct 2026
- Example dataset
- The modeling example uses alcohol-consumption data from 399 respondents in the Netherlands in 1983.checked 12 Oct 2026
- Input format
- The modeling walkthrough says its later steps can be applied when the dataset is in frequency-table form.checked 12 Oct 2026
- Installation
- The maker’s site gives installation instructions for both GitHub and CRAN.checked 12 Oct 2026
- License
- The package is listed under the GPL-2 license.checked 12 Oct 2026
- Maintainers
- The project page names Amalan Mahendran as author and maintainer and Pushpakanthie Wijekoon as author and contributor.checked 12 Oct 2026
- Model fitting
- Fitting functions can report expected frequencies, chi-squared statistics, p-values, and degrees of freedom for assessing fit.checked 12 Oct 2026
- Modeling criteria
- Its example compares candidate distributions using p-values, negative log-likelihood, AIC, observed and expected frequency differences, and variance differences.checked 12 Oct 2026
- Model selection
- The documentation recommends comparing p-values, negative log likelihood, AIC, frequency differences, and variance differences when selecting a fit.checked 12 Oct 2026
- Notable limit
- The example says steps 2 through 5 can be applied only when the dataset is in frequency-table form.checked 12 Oct 2026
- Overdispersion
- The package describes binomial mixture distributions as useful for fitting overdispersed binomial outcome data.checked 12 Oct 2026
- Purpose
- fitODBOD models binomial outcome data using binomial mixture and alternate binomial distributions.checked 12 Oct 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Support
- The maker links to a GitHub issues page for reporting bugs.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
Where it runs
- R Shiny application
- The maker says fitODBODRshiny is available to fit selected binomial outcome data using binomial mixture and alternate binomial distributions.checked 12 Oct 2026
