Analysis and Simulation of Plant Disease Progress Curves
Overview
Tools for analysis, visualization, and simulation of plant disease progress curves. Includes functions to calculate area-under-the-curve summaries, fit and compare exponential, monomolecular, logistic, and Gompertz models using linear or nonlinear regression, work with single or multiple epidemics, and produce 'ggplot2'-based visualizations. Also includes an experimental powdery mildew dataset for reproducible teaching and research workflows. See Madden, Hughes, and van den Bosch (2007) doi:10.1094/9780890545058 for background on the epidemiological methods.
Highlights
- Modernized the fitting workflow with shared input validation, more informative error messages, and improved grouped-model ergonomics.
- Refreshed the documentation site with updated articles, improved accessibility, dark mode support, and a clearer visual style for figures.
- Added a formal package citation, a vignette built around the bundled
PowderyMildewdataset, and expanded regression tests for area summaries and model fitting. - Corrected area-under-the-curve handling for repeated assessment times and strengthened defaults for nonlinear fitting workflows.
--------------------------------------------------------------------------------
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-14
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 20 (1 internal)includes a package overview page
- Examples that run
- 100%13 run in a plain check
- Exported functions with examples
- 75%
- Documented parameters
- 94%
- Return-value docs
- 100%
- Help pages citing references
- 20%
Downloads
Repository
- PRs merged (all time)
- 75%
- Median days to close, 50 most recently updated closed issues
- not measured
- Median days to close, 50 most recently updated closed PRs
- 172
Stars over time
Forks over time
Issues over time
PRs over time
5 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/alvesks/epifitter on 2026-10-11.
Dependencies
Installing epifitter adds 67 packages (41 with compiled code, 54 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.0.0Latest2021-06-14 · current release · diff ↗ ·
removed 1 export
pattern:^[[:alpha:]]+added 16 exports
AUDPC, AUDPC_2_points, AUDPS, expo_fun, fit_lin, fit_multi, fit_nlin, fit_nlin2, gompi_fun, logi_fun, mono_fun, plot_fit, sim_exponential, sim_gompertz, sim_logistic, sim_monomolecular - 0.3.02021-06-14 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 0.2.02020-11-26 · diff ↗
- 0.1.02020-10-29
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2020-10-29
- Total releases
- 4 / 6 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.1
- Bundled data
- 11 KB / 1 file
- Download size
- 140 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("epifitter")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1186/s42483-021-00098-7.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.