When can we reconstruct the ancestral state? Beyond Brownian motion

L., Vu, Nhat; Nguyễn, Thanh Phương; Nguyen, Binh T.; Dinh, Vu; Ho, Lam Si Tung

doi:10.1007/s00285-023-01922-8

J. Math. Biol.

2023

DOI: 10.1007/s00285-023-01922-8

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When can we reconstruct the ancestral state? Beyond Brownian motion

Vu, Nhat L.

Thanh Phương Nguyễn

Binh T. Nguyen

et al.

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2024

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TraitTrainR: accelerating large-scale simulation under models of continuous trait evolution

Roa Lozano,

Duncan,

McKenna

et al. 2024

Bioinformatics Advances

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Motivation The scale and scope of comparative trait data are expanding at unprecedented rates, and recent advances in evolutionary modeling and simulation sometimes struggle to match this pace. Well-organized and flexible applications for conducting large-scale simulations of evolution hold promise in this context for understanding models and more so our ability to confidently estimate them with real trait data sampled from nature. Results We introduce TraitTrainR, an R package designed to facilitate efficient, large-scale simulations under complex models of continuous trait evolution. TraitTrainR employs several output formats, supports popular trait data transformations, accommodates multi-trait evolution, and exhibits flexibility in defining input parameter space and model stacking. Moreover, TraitTrainR permits measurement error, allowing for investigation of its potential impacts on evolutionary inference. We envision a wealth of applications of TraitTrainR, and we demonstrate one such example by examining the problem of evolutionary model selection in three empirical phylogenetic case studies. Collectively, these demonstrations of applying TraitTrainR to explore problems in model selection underscores its utility and broader promise for addressing key questions, including those related to experimental design and statistical power, in comparative biology. Availability and implementation TraitTrainR is developed in R 4.4.0 and is freely available at https://github.com/radamsRHA/TraitTrainR/, which includes detailed documentation, quick-start guides, and a step-by-step tutorial.

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TraitTrainR: accelerating large-scale simulation under models of continuous trait evolution

Roa Lozano,

Duncan,

McKenna

et al. 2024

Bioinformatics Advances

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show abstract

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When can we reconstruct the ancestral state? Beyond Brownian motion

Cited by 1 publication

References 25 publications

TraitTrainR: accelerating large-scale simulation under models of continuous trait evolution

TraitTrainR: accelerating large-scale simulation under models of continuous trait evolution

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