{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T21:15:27Z","timestamp":1783372527822,"version":"3.54.6"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Behav Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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, complicating interpretation and often failing to answer research questions about the entire population directly. To address these limitations, marginalized zero-inflated (mZI) models directly estimate the population-averaged effect, yielding a single, interpretable coefficient for each predictor\u2019s 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\u00a0\n                    <jats:italic>mzim<\/jats:italic>\n                    , 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\u00a0\n                    <jats:italic>mzim<\/jats:italic>\n                    \u00a0package and compare the results from the mZINB model to traditional approaches, highlighting the practical benefits of the marginalized framework for applied researchers.\n                  <\/jats:p>","DOI":"10.3758\/s13428-026-03036-7","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T14:13:50Z","timestamp":1779459230000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating marginal effects with zero-inflated models: A tutorial with the R package mzim"],"prefix":"10.3758","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3123-4706","authenticated-orcid":false,"given":"Chendong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oi-Man","family":"Kwok","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Lawrence","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"issue":"3","key":"3036_CR1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1177\/0962280211407800","volume":"23","author":"JM Albert","year":"2014","unstructured":"Albert, J. M., Wang, W., & Nelson, S. (2014). Estimating overall exposure effects for zero-inflated regression models with application to dental caries. Statistical Methods in Medical Research, 23(3), 257\u2013278. https:\/\/doi.org\/10.1177\/0962280211407800","journal-title":"Statistical Methods in Medical Research"},{"key":"3036_CR2","doi-asserted-by":"publisher","unstructured":"Agresti, A. (2002).\u00a0Categorical data analysis\u00a0(2nd ed.). John Wiley & Sons. https:\/\/doi.org\/10.1002\/0471249688","DOI":"10.1002\/0471249688"},{"issue":"5","key":"3036_CR3","doi-asserted-by":"publisher","first-page":"376","DOI":"10.3109\/00952990.2011.568080","volume":"37","author":"D Bandyopadhyay","year":"2011","unstructured":"Bandyopadhyay, D., DeSantis, S. M., Korte, J. E., & Brady, K. T. (2011). Some considerations for excess zeroes in substance abuse research. 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As this study utilizes pre-existing, anonymized data, specific ethical approval for this analysis was not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest\/Competing interests"}},{"value":"The analysis code and the software package\n                      mzim\n                      described in this article are available at\n                      \n                      . This study was not preregistered.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Open practices statement"}}],"article-number":"169"}}