Author

David I. Warton

UNSW SydneyORCID 0000-0001-9441-6645Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Ecology· 1 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:development & morphogenesis· 1 Research Focus:phylogenetics / evolution· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Daniel S. FalsteriD· 2 shared Theresa O’BrieniD· 2 shared Benjamin M. BolkeriD· 1 shared Coralie WilliamsiD· 1 shared Fonti KariD· 1 shared Maeve McGillycuddyiD· 1 shared Shinichi NakagawaiD· 1 shared Szymon Marian DrobniakiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 13 mutual co-author links

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2 shared papersBioinformatics

Daniel S. FalsterORCID

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Author profile →

3 papers

Recent·Most cited

Fast phylogenetic generalised linear mixed-effects modelling using the glmmTMB R package

Coralie Williams, Maeve McGillycuddy, Szymon Marian Drobniak, Benjamin M. Bolker, David I. Warton, Shinichi Nakagawa

The paper compares R packages for phylogenetic generalised linear mixed models and shows glmmTMB is faster and more flexible for fitting PGLMMs with likelihood-based models.

bioRxiv · Feb 4, 2026 · PreprintEvolutionary Biology

hmde: Hierarchical Methods for Differential Equations

Tess O’Brien, Fonti Kar, David I. Warton, Daniel S. Falster

The paper introduces an R package for hierarchical Bayesian longitudinal modeling of growth data, providing tools for model fitting and case studies.

bioRxiv · Jan 15, 2025 · PreprintBioinformatics

Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size

Theresa O’Brien, David I. Warton, Daniel S. Falster

This paper introduces a hierarchical Bayesian model to improve growth and size estimates from repeat forest surveys by accounting for measurement error and individual variation.

Methods in Ecology and Evolution · Nov 24, 2024Peer-reviewedEcology