Fitting Functional Responses in 1- and 2-Prey Systems
Overview
Easy application of Bayesian inference for functional responses via 'brms'. This package allows to fit various FR models for single- and multi-prey experiments by providing nonlinear prediction functions for 'brms'. It uses dynamical prediction models to correct for prey depletion. The 'brms' framework facilitates statistical modeling and enables users to conveniently incorporate covariates such as temperature gradients, experimental treatment variables, or random effects that account for grouping in experimental units. Default 'brms' functions make it easy to perform model checking, model comparison and hypothesis testing. Potential statistical issues with data from feeding trials, such as overdispersion, can be resolved by effortlessly switching between likelihood functions. This package, together with its tutorials, should provide students and researchers with a comprehensive and integrated statistical framework for easily testing their hypotheses on trophic interactions. References: Rosenbaum and Rall (2018) doi:10.1111/2041-210X.13039; Rosenbaum et al. (2024) doi:10.1111/2041-210X.14372.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK8 OK2026-06-24
Documentation
- Help pages
- 29
- Examples that run
- 100%9 run in a plain check
- Exported functions with examples
- 0%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 14%
Downloads
Repository
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Checks run against github.com/benjamin-rosenbaum/bayesfr on 2026-10-21.
GitHub Pages site last deployed 2026-06-24
Dependencies
Code & Tests
Datasets
People & History
- 1.0.1Latest2026-06-23 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-23
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 8.7 KB / 9 files
- Download size
- 2.7 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("BayesFR")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1111/2041-210x.13039, 10.1111/2041-210x.14372.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.