R-package agentBayes: likelihood-based statistical methods for agent-based models

R-package agentBayes: likelihood-based statistical methods for agent-based models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article R-package agentBayes: likelihood-based statistical methods for agent-based models Niklas Moser, Dmitri Finkelshtein, Georgy Chargaziya, Stephen Cornell, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8910712/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Statistically analysing interacting particle systems remains challenging because the governing equations are analytically intractable. Existing solutions include moment closure methods with pseudolikelihood-based frameworks, and likelihood-free frameworks based on extensive simulations, both relying on heuristic choices whose validity is difficult to predict. As a resolution, we rigorously derive an asymptotically exact expression for the likelihood of agent-based models (ABMs). Our framework applies to ABMs formulated as reactant–catalyst–product (RCP) models in continuous space and time. We derive an expression for the conditional density of agents given information about the current and earlier distributions of neighbouring agents. We utilize this expression to construct an asymptotically exact likelihood that applies to both spatial snapshot and time-series data. We implement the likelihood expression and a Bayesian parameter estimation framework in the here introduced R-package agentBayes and demonstrate its utility in biological research and beyond with simulated case studies and empirical data on the evolution of cancer cell populations. Biological sciences/Computational biology and bioinformatics/Statistical methods Biological sciences/Computational biology and bioinformatics/Software Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementaryinformationABMlikelihood.pdf Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8910712","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":609108763,"identity":"93eb9a9f-67f6-48e9-a625-175915e254dc","order_by":0,"name":"Niklas 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