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.
```r # Install from GitHub using pak (recommended) install.packages(“pak”) pak::pkg_install(“Segon03/funresMech”)
install.packages(“devtools”) devtools::install_github(“Segon03/funresMech”) From CRAN (stable version, once published) r install.packages(“funresMech”) Basic Usage Launch the Shiny App r library(funresMech) run_app() This opens the interactive application where you can:
Upload your dataset (CSV format).
Select columns for species, host density, and parasitism.
Configure advanced settings (simulation parameters, optimization options).
Run the analysis and explore results interactively.
Programmatic Usage (Advanced) r # Load the package library(funresMech)
data <- data.frame( species = rep(“Species_A”, 30), dens = rep(c(10, 20, 40, 80, 160), each = 6), par = c(2, 3, 5, 8, 12, …) # Your data )
Features Mechanistic simulation: Search times follow a Gamma distribution; handling times follow a Lognormal distribution.
Stochastic likelihood: The probability distribution of parasitism is generated through repeated simulations.
Flexible density scaling: The exponent (z) allows emergence of Type I, II, III-like responses.
Uncertainty quantification: Confidence intervals for (z) via profile likelihood.
Interactive visualization: Dynamic plots with plotly for exploring results.
Comprehensive reporting: Generate HTML reports summarizing all analyses.
Documentation Full documentation is available within the package:
r # View package documentation help(package = “funresMech”)
?run_app Citation If you use funresMech in your research, please cite:
bibtex @article{NunezCampero2026, author = {Segundo Núñez-Campero}, title = {funresMech: Mechanistic Functional Response Analysis using the Okuyama Model}, year = {2026}, note = {R package version 1.0.4}, 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 This package is distributed under the MIT License:
YEAR: 2026
COPYRIGHT HOLDER: Segundo Núñez-Campero
For more details, see the LICENSE file.
Contributing Contributions are welcome! Please feel free to 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.