Estimating Marginal Effects with Zero-inflated Models: A Tutorial with R package mzim

Count data in the psychological and health sciences are often characterized by an excess of zero values, a feature known as zero-inflation. While traditional zero-inflated models, such as the Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB), were developed to handle such data, they present challenges for applied researchers. Standard count models can produce biased estimates, and the dual-parameter output of traditional zero-inflated models provides conditional effects for a latent at-risk subpopulation, which complicates interpretation and often fails to directly answer research questions about the entire population. To address these limitations, marginalized zero-inflated (mZI) models directly estimate the population-averaged effect, yielding a single, interpretable coefficient for each predictor's overall effect. However, the adoption of mZI models has been hindered by the lack of an accessible software package. The current study has two objectives: first, it provides a tutorial on the theory, estimation, and interpretation of marginalized zero-inflated models. Second, it introduces mzim , a new R package designed to make both marginalized zero-inflated Poisson (mZIP) and Negative Binomial (mZINB) models readily accessible. Using an empirical example on self-reported youth abuse experiences, we demonstrate a complete workflow with the mzim package and compare the results from the mZINB model to traditional approaches, highlighting the practical benefits of the marginalized framework for applied researchers.