In pjbouchet/espresso: Bayesian Inference For Multi-Species Behavioural Dose-response Functions

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Multi-species Bayesian dose-response models

espresso stands for Estimating Shared Patterns of RESponsiveness to Navy SOnar, and was designed as a toolkit for fitting and selecting among behavioural dose-response functions in cetaceans exposed to anthropogenic sound.

Rationale

This work builds upon previous research completed under the U.S. Navy-funded MOCHA project [@Harris2016; @Harris2018], in which Bayesian hierarchical models were developed to estimate the probabilities of noise-related behavioural impacts to individual marine mammals, whilst accounting for uncertainty and the effects of contextual covariates [@Miller2014; @Antunes2014]. The current modelling framework is implemented in the Bayesian analysis software JAGS (https://mcmc-jags.sourceforge.io/), and relies on Gibbs Variable Selection [@OHara2009] to identify groups of species exhibiting similar patterns of responsiveness to impulsive sound stimuli. However, this approach proves computationally intractable for more than a few species and/or covariates. espresso uses a bespoke dimension-jumping reversible-jump Markov chain Monte Carlo algorithm [rjMCMC, @Green1995; @Hastie2012] to relax these constraints and allow species groupings to be identified in an objective, data-driven way. The package also accommodates: (1) the selection of any number of explanatory covariates (e.g., sonar frequency, previous history of exposure, feeding behaviour, source-whale range), (2) the comparison of dose-response functional forms (i.e., monophasic or biphasic [soon to be released]), and (3) the appropriate treatment of both left- and right-censored observations (i.e., animals which display either an immediate response on first exposure, or no signs of response across the array of doses received, respectively).

Getting started

If you are just getting started with espresso, we recommend reading the tutorial vignette, which provides a quick introduction to the package.

Installation

Install the GitHub development version to access the latest features and patches.

# install.packages("remotes")
remotes::install_github("pjbouchet/espresso") # OR

# install.packages("devtools")
devtools::install_github("pjbouchet/espresso")

The package relies on compiled code (C++) and functionalities provided by the Rcpp package. The Rtools software may be needed on Windows machines. Installation instructions can be found at https://cran.r-project.org/bin/windows/Rtools/rtools40.html.

References

pjbouchet/espresso documentation built on July 27, 2024, 12:31 p.m.