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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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