TraitTrainR: Accelerating large-scale simulation under models of continuous trait evolution DOI Creative Commons

Jenniffer Roa Lozano,

Mataya Duncan,

Duane D. McKenna

и другие.

Bioinformatics Advances, Год журнала: 2024, Номер 5(1)

Опубликована: Дек. 9, 2024

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

Язык: Английский

TraitTrainR: Accelerating large-scale simulation under models of continuous trait evolution DOI Creative Commons

Jenniffer Roa Lozano,

Mataya Duncan,

Duane D. McKenna

и другие.

Bioinformatics Advances, Год журнала: 2024, Номер 5(1)

Опубликована: Дек. 9, 2024

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

Язык: Английский

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