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BayesFR

1.0.1Package record

Fitting Functional Responses in 1- and 2-Prey Systems

0packages depend
1.3Kdownloads / year
0.0%test coverage
13/13flavors OK

Overview

About

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

CRAN checks
13OK
Slowest check: 3.3 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK8 OK2026-06-24
Advisories

none known

Documentation

In release 1.0.1
READMEYes · 366 wordsVignettesYes · 1 · dynamicNEWSNoCitation fileYes · cites a publication
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

1.3K
CRAN downloads in the past year
Rank #12,764 · ~4/day · ~111/mo
Daily download trend is not available in this view yet.
30830 days
88390 days
1.3K1 year
Compare downloads with other packages →
Also on110 r2u25 autocran

Repository

1Stars
0Forks
0Open issues
0Open PRs
2Releases
106Commits
1Commit identities
106 commits · Last activity 2026-06-24 · 0% stars, 30d
Responsiveness
not measured

Stars over time

2025-12-19 · 12026-07-07 · 1
PracticesBeta

5 development-tooling and community-health practices detected across 4 families in the upstream repository

Checks run against github.com/benjamin-rosenbaum/bayesfr on 2026-10-21.

Continuous integration (1)
GitHub Actions
Docs source (1)

GitHub Pages site last deployed 2026-06-24

CRAN release process (2)
cran-comments.mdCRAN-SUBMISSION
Lint, format, editor (1)
RStudio project
How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)

Installing BayesFR adds 72 packages (35 with compiled code, 75 MB of source tarballs). Heaviest: BH 14 MB, stringi 11 MB, ggplot2 6.0 MB.

Depends (1)
R >= 3.5
Imports (3)
LinkingTo (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (1)
Author, Maintainer since 1.0.1 (2026)
Releases (1)
  • 1.0.1Latest
    2026-06-23 · current release
  • R
    R 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.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for BayesFR version 1.0.1 [Data set]. HJJB, LLC. Data release v2026-10-23. https://doi.org/10.5281/zenodo.21843040

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.

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