squidSim: a flexible R package for structured and reproducible simulations in Ecology and Evolutionary Biology

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. 1. Complex statistical methodology now allows a growing array of questions to be addressed in ecology and evolutionary biology. However, the particular question being addressed or the complex nature of the data collected often raise issues with how statistical models perform and potentially limit inference. Simulations provide a powerful approach to help empiricists understand the assumptions, limitations, and output of generalised linear mixed models (GLMMs), advance teaching of statistical modelling and design more informed studies around their usage. 2. Datasets in ecology and evolutionary biology often have complex hierarchical structures, which create challenges in creating simulations. This problem is exacerbated by the current lack of flexible and reproducible tools that facilitate simulating complex data from a wide range of data structures. 3. Here we present the squidSim R package, a flexible and logical program designed to accommodate many of the common data structures in ecology and evolutionary biology. The program can simulate from a wide diversity of models in a generalised linear mixed model (GLMM) framework, including data from Gaussian and non-Gaussian models, multi-response models, as well as spatial, temporal, genetic and phylogenetic effects. 4. In addition to facilitating simulations for a wide range of models and data structures, squidSim R package provides a fully reproducible workflow and has established utility for teaching. We also provide a graphical user interface via the shinySim R package. https://doi.org/10.32942/X20M0T Ecology and Evolutionary Biology, Statistical Methodology reproducibility, multivariate, autocorrelation, phylogeny, pedigree, genetic variation, random effects, Hierarchical Models, linear models, Simulation Published: 2025-09-10 23:51 Last Updated: 2025-09-16 12:41 CC BY Attribution 4.0 International Conflict of interest statement: The authors declare no conflict of interest. Data and Code Availability Statement: All code for the simulated examples are deposited in \url{https://github.com/squidgroup/squidSim_manuscript} Language: English