| Title: | Mechanistic Functional Response Analysis |
| Version: | 1.1.0 |
| Description: | Implements the mechanistic functional response model proposed by Okuyama (2012) <doi:10.1016/j.biocontrol.2011.10.008> for host-parasitoid systems. Provides tools for model fitting, likelihood profiling, stochastic simulation, and visualization of parasitism distributions. The stochastic simulation engine is implemented in C++ via 'Rcpp'. Includes an interactive 'shiny' application for complete analysis workflows, built with 'plotly' for interactive graphics and 'rmarkdown' for reproducible reports. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Depends: | R (≥ 4.1.0) |
| Imports: | DEoptim, dplyr, future, ggplot2 (≥ 3.4.0), magrittr, parallel, plotly, Rcpp, rlang, rmarkdown, shiny, shinyBS, shinythemes, stats, utils |
| LinkingTo: | Rcpp |
| Suggests: | kableExtra, knitr, testthat (≥ 3.0.0) |
| URL: | https://github.com/Segon03/funresMech |
| BugReports: | https://github.com/Segon03/funresMech/issues |
| Config/testthat/edition: | 3 |
| Config/Needs/check: | r-lib/actions/dependencies |
| Config/roxygen2/version: | 8.1.0 |
| NeedsCompilation: | yes |
| Packaged: | 2026-10-06 00:11:08 UTC; Equipo |
| Author: | Segundo Núñez-Campero [aut, cre] |
| Maintainer: | Segundo Núñez-Campero <segundo.nc@conicet.gov.ar> |
| Repository: | CRAN |
| Date/Publication: | 2026-10-06 02:20:02 UTC |
funresMech: Mechanistic Functional Response Analysis
Description
Implements the mechanistic, stochastic functional response model of Okuyama (2012, 2026) for host-parasitoid systems. The probability distribution of the number of parasitised hosts is obtained by simulating the search-encounter-handling process (Gamma search times, Lognormal handling times), and the parameters are estimated by maximum likelihood with a simulated likelihood. The simulation engine is written in C++ with 'Rcpp'.
Details
The package is used through the interactive application started with
run_app(), which fits the model, computes the likelihood profile of the
density-scaling exponent z (95% interval by the likelihood-ratio
criterion), compares the model with free z against z = 1 by AIC,
reports diagnostics and screens atypical trials.
Author(s)
Maintainer: Segundo Núñez-Campero segundo.nc@conicet.gov.ar
Authors:
Segundo Núñez-Campero segundo.nc@conicet.gov.ar
References
Okuyama, T. (2012). A likelihood approach for functional response models. Biological Control, 60(2), 103-107. doi:10.1016/j.biocontrol.2011.10.008
Okuyama, T. (2026). Parametric assumptions in parasitoid functional response analysis. Journal of Applied Entomology. doi:10.1111/jen.70148
See Also
Run the funresMech Shiny App
Description
Launches the interactive application for mechanistic functional response analysis, based on the Okuyama model.
Usage
run_app(...)
Arguments
... |
Additional arguments passed to |
Details
The application fits the model to a table with one row per trial (species,
host density and number of parasitised hosts) and offers the following
tabs: fitted parameters, likelihood profile of the density-scaling exponent
z with its 95% interval, stochastic curves and distributions, diagnostics
(small k, parameters on a bound, open intervals), screening of atypical
trials, and an HTML report. The parameter s is the standard deviation of
the handling time on the natural scale (same units as h).
The profile is computed in parallel with the 'future' package, so results
are reproducible with set.seed() before launching the app.
Value
This function launches a Shiny app and does not return a value.
References
Okuyama, T. (2012). A likelihood approach for functional response models. Biological Control, 60(2), 103-107. doi:10.1016/j.biocontrol.2011.10.008
Okuyama, T. (2026). Parametric assumptions in parasitoid functional response analysis. Journal of Applied Entomology. doi:10.1111/jen.70148
Examples
if (interactive()) {
run_app()
}