Author

Tess O’Brien

UNSW SydneyORCID 0009-0000-5821-8801Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Ecology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:development & morphogenesis· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Daniel S. FalsteriD· 2 shared David I. WartoniD· 2 shared Fonti KariD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

3 top collaborators · 3 mutual co-author links

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2 shared papersBioinformatics

Daniel S. FalsterORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJan 15, 2025

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.

DOI 10.1101/2025.01.15.633280

statistical modelsoftware / tool

𝕏 PostBluesky

EcologyMethods in Ecology and EvolutionPeer-reviewedNov 24, 2024

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.

1 citation

statistical modeldevelopment & morphogenesis

𝕏 PostBluesky