Corncob: Beta-Binomial Count Regression for Correlated Data

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020). The method allows for covariates to model both the mean and overdispersion of the data. The dataset likely contains the statistical models and associated data used for the described regression analysis.

Use Cases

• Modeling overdispersed count data based on the beta-binomial distribution.
• Fitting regression models with covariates for both mean and overdispersion parameters.
• Analyzing correlated observations where standard Poisson regression is insufficient.

Strengths

• Methodology is peer-reviewed and published in a statistical journal (Martin et al., 2020).
• Explicitly handles overdispersion and correlation, common challenges in count data analysis.

Limitations

• Row count and data scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Bryan D Martin, associated with the paper 'Count Regression for Correlated Observations with the Beta-Binomial'.

Collection Method

Likely contains data or code for implementing the described statistical models.