Zero-Inflated Model Lab: An Interactive Shiny Application for Modelling Count Data with Excess Zeros
Abstract
Count data often contain a large number of zeros in ecological and environmental research, arising from factors such as true species absence, imperfect detection, or the rarity of certain events. Choosing and interpreting suitable statistical models, like Poisson, Negative Binomial, zero-inflated, or hurdle models, can be difficult for non-experts because of the models' assumptions and diagnostic requirements. To address this, we introduce an interactive web application built in R with the Shiny framework that helps users analyse, compare, and interpret count-data models that may exhibit zero inflation. The application enables users to upload datasets, explore distributions and relationships among variables, and fit a variety of models including Poisson, Negative Binomial, zero-inflated, and hurdle approaches. It incorporates tools for model comparison using information criteria, visualisation of coefficient estimates and confidence intervals, assessment of variable importance, and diagnostic checks based on simulated residuals. Importantly, the app provides clear visualisations of both the count-generating and zero-generating processes, making it easier to communicate components that are typically challenging to interpret. To improve accessibility, the tool offers guided explanations of outputs and diagnostics, making it valuable for both research applications and teaching. A case study using environmental count data illustrates how the application helps users identify zero inflation, choose appropriate models, and gain insight into the ecological processes underlying the data. Overall, the application narrows the gap between advanced statistical methods and practical analysis, supporting reproducible workflows and enhancing the clarity and interpretability of models for zero-inflated count data.
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BibTeX
@misc{rivera2026zeroinflated,
title = {{Zero-Inflated Model Lab: An Interactive Shiny Application for Modelling Count Data with Excess Zeros}},
author = {Oscar Rodriguez de Rivera},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14772},
eprint = {2609.14772},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14772},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Zero-Inflated Model Lab: An Interactive Shiny Application for Modelling Count Data with Excess Zeros
AU - Oscar Rodriguez de Rivera
PY - 2026
DA - 2026/09/13
PB - arXiv
DO - 10.48550/arxiv.2609.14772
UR - https://arxiv.org/abs/2609.14772
AB - Count data often contain a large number of zeros in ecological and environmental research, arising from factors such as true species absence, imperfect detection, or the rarity of certain events. Choosing and interpreting suitable statistical models, like Poisson, Negative Binomial, zero-inflated, or hurdle models, can be difficult for non-experts because of the models' assumptions and diagnostic requirements. To address this, we introduce an interactive web application built in R with the Shiny framework that helps users analyse, compare, and interpret count-data models that may exhibit zero inflation. The application enables users to upload datasets, explore distributions and relationships among variables, and fit a variety of models including Poisson, Negative Binomial, zero-inflated, and hurdle approaches. It incorporates tools for model comparison using information criteria, visualisation of coefficient estimates and confidence intervals, assessment of variable importance, and diagnostic checks based on simulated residuals. Importantly, the app provides clear visualisations of both the count-generating and zero-generating processes, making it easier to communicate components that are typically challenging to interpret. To improve accessibility, the tool offers guided explanations of outputs and diagnostics, making it valuable for both research applications and teaching. A case study using environmental count data illustrates how the application helps users identify zero inflation, choose appropriate models, and gain insight into the ecological processes underlying the data. Overall, the application narrows the gap between advanced statistical methods and practical analysis, supporting reproducible workflows and enhancing the clarity and interpretability of models for zero-inflated count data.
ER -