underdisp: Diagnostics and Models for Underdispersed Count Data

Detect and model underdispersion (conditional variance below the conditional mean) in count data.

Screening and diagnostics

ud_screen() (the zero-truncated-Poisson at-risk screen with calibrated or parametric-bootstrap thresholds), dispersion_test() (regression-adjusted tests of equidispersion), dispersion_profile() (the conditional variance-to-mean curve against each family's implied curve), compare_dispersion(), zi_test(), rootogram(), pit_hist().

Estimators

The hard-ceiling family cpb() / cpb_fe() and the free-dispersion family gec() / gec_fe(), with hurdle and zero-inflated forms (hurdle_cpb(), zi_cpb(), hurdle_gec(), zi_gec()); the matched count families through one interface, count_reg(), hurdle_count(), zi_count() (Poisson, negative binomial, COM-Poisson in the rate and the mean parameterization, generalized Poisson, gamma-count, double Poisson), all with offsets, frequency weights, fixed effects, and analytic, robust, cluster, or bootstrap inference.

Comparison and quantities of interest

compare_models(), score(), cv_score(); predict(), implied_ceiling(), irr(), first_difference() (with the extensive/intensive decomposition for the two-part models), confint() for every class, simulate() for DHARMa diagnostics, and broom / texreg / modelsummary support.

See also

Useful links:

• https://CRAN.R-project.org/package=underdisp

• https://github.com/bagozzib/underdisp

• https://bagozzib.github.io/underdisp/

• Report bugs at https://github.com/bagozzib/underdisp/issues

Author

Maintainer: Benjamin E. Bagozzi bagozzib@udel.edu (ORCID)