Revticulate: An R framework for interaction with RevBayes. Issue 6 (11th April 2022)

• Record Type: Journal Article
• Authors: Charpentier, Caleb P.; Wright, April M.
• Is Part Of: Methods in ecology and evolution. Volume 13:Issue 6(2022)
• Page Start: 1177
• Page End: 1184
• Publication Date: 2022-04-11
• Subjects: Bayesian phylogenetics -- phylogeny -- R packages -- RevBayes; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13852
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: Phylogenetic methods are increasingly complex. Researchers need to make many choices about how to model different aspects of the data appropriately. It is increasingly common to deploy hierarchical Bayesian models in which different data types may be described by different processes. This necessitates tools to help users understand model assumptions more clearly. We describe the package Revticulate, which provides an R‐based interface to the software RevBayes. RevBayes is a Bayesian phylogenetic program that implements an R‐like computing language, but does not interface with R itself. Revticulate was designed to allow communication between an R session, and all of its associated capabilities, such as plotting and simulation, and a RevBayes session. Revticulate can be used to copy objects from RevBayes into R. We provide several usage examples demonstrating how objects, such as random variables drawn from probability distributions and phylogenetic trees, can be generated in RevBayes. We then show how these objects can be used with R's phylogenetic ecosystem to plot a phylogenetic tree, or with base R functions to simulate the behaviour of a particular probability. Revticulate is a broadly useful software. Revticulate can be used alongside popular document preparation packages, such as knitr and pkgdown to generate attractive reports, tutorials and websites. This means that researchers who are looking to communicate their work in RevBayes can do that very easily using Revticulate, enabling rapid generation of reproducible research outputs.