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epiphytoolR

An R package that provides functions for undertaking epiphytotic modelling and simulation studies in R.

epiphytoolR aims

This package aims to be a collaborative project to provide a suite of functions for epidemiologists to build models from. The functions should be at their most basic and easy to adapt to the various epidemiological models. We hope this becomes a dependency for many more models. We welcome contributions additional branches to explore additional functionality and features.

What epiphytoolR is not

This does not provide full epidemiological models. See the following R packages for flexible complete models:

my suggestions

Weather

CRAN Task views

Co-pilot suggestions

not yet vetted
- EpiDynamics
- EpiILM
- EpiNow2
- EpiEstim
- EpiCurve
- EpiModel
- epiR
- epiDisplay

How to install

epiphytoolR is not on CRAN but is aimed for a release sometime in 2025.

You can install a stable version from the main branch with the following code

remotes::install_github("PaulMelloy/epiphytoolR")

Or you can install the development version from the development (dev) branch with the following command in R

remotes::install_github("PaulMelloy/epiphytoolR", ref = "dev")

The dev branch will have newer bug fixes and more features, see NEWS.md. However may have new bugs yet to be fully tested

How to contribute

Reporting bugs

Create an issue on this repo.

Contributing code

  1. Fork the repo make changes and create a pull request.
  2. Request contribute access in the pull request.

Contributing is easy, you may notice the reuse of common functions in plant epidemiological modelling. Translate these equations to R code and add them to this package through a pull request.

Contribution Example

y=a*(x+c)−by=a*(x+c)^{-b}

can become a function

calc_spores <- function(x, a, con, b){
   return(a * (x + con) ^ (-b))
}

Also use roxygen headers to describe the function in the file. ‘Ctrl + Alt + Shift + R’ Inserts an Roxygen header skeleton to edit.

#' Estimate spore dispersal
#'
#' @description
#' A modified inverse power function redescribed by Severns et al. (2018).
#'  "Consequences of Long-Distance Dispersal for Epidemic Spread: Patterns,
#'   Scaling, and Mitigation"
#'
#'
#' @source <https://doi.org/10.1094/PDIS-03-18-0505-FE>
#'
#' @param x numeric, distance from source lesion in meters
#' @param a numeric, a constant proportional to the amount of source inoculum
#' @param con numeric, a constant that allows for a finite amount of disease at the source
#' @param b numeric, modifies the steepness of the curve
#'
#' @return numeric, The number of effective spores, or infections per unit area
#' @export
#'
#' @examples
#' calc_spores(x = 5,a = 1000, con = 3, b = 4)

Function naming

Function naming should be concise and have the consistent use of prefixes as follows. - est_ estimate, usually used when the function or equation is based on empirical evidence.
- get_ a wrapper for retrieving data or accessing an API, or calculations form data. - impute_ imputation functions.
- calc_ Calculate from common equations.

Main features

Inoculum dispersal and movement

This package provides common base level functions that can be used in modelling the spatial movement of splash and wind dispersed plant disease inoculum.

Disease progress

Estimating Daily Infection Values, AUDPC, ect.

Weather formatting

Epidemiological models need clean consistent weather data, this package contains functions to provide clean interpolated data for models.

Future versions will look towards an international standard for weather data format.

Find out more

For more information see pkgdown website epiphytoolR