Package: underdisp 0.1.2

underdisp: Diagnostics and Models for Underdispersed Count Data

Tools for detecting and modeling underdispersion in count data (conditional variance below the conditional mean), the case the Poisson and negative binomial defaults cannot represent. Provides a screening diagnostic that benchmarks at-risk dispersion against a zero-truncated Poisson, regression-adjusted tests of equidispersion, and a dispersion profile that compares the variance-to-mean curves of competing families against the data; the continuous parameter binomial (CPB) and generalized event count (Katz) regressions with zero-truncated, hurdle, and zero-inflated forms and high-dimensional fixed effects with a split-panel jackknife bias correction; matched Poisson, negative binomial, COM-Poisson (rate- and mean-parameterized), generalized Poisson, gamma-count, and double Poisson regressions through the same interface, with frequency weights, offsets, and analytic, robust, and cluster-robust standard errors; bootstrap and profile-likelihood inference; proper scoring rules, rootograms, PIT histograms, and simulation methods; and quantities of interest including predicted distributions, the implied ceiling, rate ratios, and first differences with an extensive/intensive decomposition. The likelihoods are implemented in C++.

Authors:Benjamin E. Bagozzi [aut, cre]