boral – Bayesian Ordination and Regression Analysis of Multivariate Abundance Data in r

Francis K.C. Hui*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    220 Citations (Scopus)

    Abstract

    Model-based methods have emerged as a powerful approach for analysing multivariate abundance data in community ecology. Key applications include model-based ordination, modelling the various sources of correlations across species, and making inferences while accounting for these between species correlations. boral (version 0.9.1, licence GPL-2) is an r package available on cran for model-based analysis of multivariate abundance data, with estimation performed using Bayesian Markov chain Monte Carlo methods. A key feature of the boral package is the ability to incorporate latent variables as a parsimonious method of modelling between species correlation. Pure latent variable models offer a model-based approach to unconstrained ordination, for visualizing sites and the indicator species characterizing them on a low-dimensional plot. Correlated response models consist of fitting generalized linear models to each species, while including latent variables to account for residual correlation between species, for example, due to unmeasured covariates.

    Original languageEnglish
    Pages (from-to)744-750
    Number of pages7
    JournalMethods in Ecology and Evolution
    Volume7
    Issue number6
    DOIs
    Publication statusPublished - 1 Jun 2016

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