funresMech: Mechanistic Functional Response Analysis using the Okuyama Model

Overview

funresMech implements the mechanistic, stochastic functional response model proposed by Okuyama (2012) and extended to parasitoids in Okuyama (2026). Unlike traditional approaches that rely on heuristic distributions (binomial or beta-binomial), this package simulates the underlying search-encounter-handling process to generate the probability distribution of the data, providing a more flexible and mechanistically sound framework for functional response analysis.

The package includes: - An interactive Shiny application for data exploration and model fitting. - Maximum likelihood estimation using a simulation-based likelihood. - Likelihood profiles for the density-scaling exponent z. - Model comparison via AIC between full (z free) and restricted (\(z = 1\)) models. - Comprehensive diagnostic plots including stochastic curves, histograms, density plots, boxplots, violins, and fan plots.

Installation

From CRAN

install.packages("funresMech")

From GitHub (development version)

# Using pak (recommended)
install.packages("pak")
pak::pkg_install("Segon03/funresMech")

# Or using devtools
install.packages("devtools")
devtools::install_github("Segon03/funresMech")

Since version 1.1.0 the package contains compiled code (C++ via ‘Rcpp’). On Windows and macOS, installing from GitHub requires a compiler (Rtools / Xcode command line tools); the CRAN version is distributed as a binary.

Usage

Launch the Shiny app

library(funresMech)
run_app()

In the app you can:

1. Upload your dataset (CSV): one row per trial with species, host density and number of parasitised hosts.
2. Select the columns, the experiment duration T and the grid of z.
3. Run the analysis: parameters, likelihood profile of z with its 95% interval, AIC (free z vs z = 1), stochastic curves and distributions.
4. Review the Diagnostics and Data screening tabs, and download an HTML report.

For reproducible results call set.seed() before run_app(); the likelihood profile is computed in parallel and gives the same result as a sequential run with the same seed.

Features

• Mechanistic simulation: search times follow a Gamma distribution; handling times follow a Lognormal distribution with mean h and standard deviation s (natural scale, as in Okuyama 2026). The engine is written in C++ (‘Rcpp’).
• Simulated likelihood: the probability distribution of parasitism is generated through repeated simulations.
• Flexible density scaling: the exponent z allows Type I, II and III-like responses.
• Uncertainty: 95% interval of z from the likelihood profile (likelihood-ratio threshold of 1.92 log-likelihood units), with automatic extension and refinement of the grid when a limit is open.
• Diagnostics and screening of atypical trials (data are never removed).
• Interactive visualisation with ‘plotly’ and reports in HTML.

What is new in 1.1.0

Version 1.1.0 replaces the computational core and changes some results with respect to 1.0.4 (scale of s, threshold of the interval, search on the log scale). See NEWS.md for the details.

Documentation

help(package = "funresMech")
?funresMech
?run_app

Citation

If you use funresMech in your research, please cite:

@article{NunezCampero2026,
  author = {Segundo Núñez-Campero},
  title = {funresMech: Mechanistic Functional Response Analysis using the Okuyama Model},
  year = {2026},
  note = {R package version 1.1.0},
  url = {https://github.com/Segon03/funresMech}
}

@article{Okuyama2012,
  author = {Okuyama, Toshinori},
  title = {A likelihood approach for functional response models},
  journal = {Biological Control},
  volume = {60},
  number = {2},
  pages = {103--107},
  year = {2012},
  doi = {10.1016/j.biocontrol.2011.10.008}
}

@article{Okuyama2026,
  author = {Okuyama, Toshinori},
  title = {Parametric Assumptions in Parasitoid Functional Response Analysis},
  journal = {Journal of Applied Entomology},
  year = {2026},
  doi = {10.1111/jen.70148}
}

License

MIT License (see the LICENSE file). Copyright holder: Segundo Núñez-Campero.

Contributing

Contributions are welcome. Please submit issues, feature requests or pull requests on GitHub.

References

Okuyama, T. (2012). A likelihood approach for functional response models. Biological Control, 60(2), 103-107.

Okuyama, T. (2026). Parametric Assumptions in Parasitoid Functional Response Analysis. Journal of Applied Entomology.