glmOJ: Toolkit for Count Regression Modeling

Provides a workflow for fitting, diagnosing, and interpreting count and semi-continuous regression models. Supports Poisson, quasi-Poisson, negative binomial, Tweedie, zero-inflated Poisson, zero-inflated negative binomial, and zero-inflated Tweedie models. Includes data summarization tools, randomized quantile residual diagnostics, and a general-purpose wrapper (countGLM()) that fits all model families and selects the best by AIC, BIC, log-likelihood, or McFadden pseudo-R².

See also

Useful links:

• https://github.com/ojaroker/glmOJ

• http://oscar.jaroker.com/glmOJ/

• Report bugs at https://github.com/ojaroker/glmOJ/issues

Author

Maintainer: Oscar Jaroker ojaroker@colgate.edu