R package countimp: Multiple Imputation of incomplete count data. Special data types like count data require special analysis and imputation techniques. Yet, currently available multiple imputation tools are very limited with regard to count data. The countimp package provides easy to use multiple imputation (MI) procedures for incomplete count data based on either a Bayesian regression approach (Rubin, 1987) or on a bootstrap regression approach within a chained equations MI framework (van Buuren, Brand, GroothuisOudshoorn, & Rubin, 2006; van Buuren & Groothuis-Oudshoorn, 2011). Our software works as an add-on for the popular and powerful mice package in R (van Buuren & GroothuisOudshoorn, 2011). The current version of countimp supports ordinary count data imputation under the Poisson model, imputation of incomplete overdispersed count data under either the Quasi-Poisson or the Negative Binomial model, imputation of zero-inflated ordinary or overdispersed count data based on a zero-inflated Poisson or Negative Binomial model, and imputation of multilevel count data based on a generalized linear mixed effects model (overdispersion and zero-inflation are supported).

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  1. Quartagno, Matteo; Carpenter, James R.: Multiple imputation for discrete data: evaluation of the joint latent normal model (2019)