Package: epifitter 1.0.1

Kaique dos S. Alves

epifitter: Analysis and Simulation of Plant Disease Progress Curves

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.

Authors:Kaique dos S. Alves [aut, cre], Emerson M. Del Ponte [aut], Adam H. Sparks [aut]

epifitter_1.0.1.tar.gz
epifitter_1.0.1.zip(r-4.7)epifitter_1.0.1.zip(r-4.6)epifitter_1.0.1.zip(r-4.5)
epifitter_1.0.1.tgz(r-4.6-any)epifitter_1.0.1.tgz(r-4.5-any)
epifitter_1.0.1.tar.gz(r-4.7-any)epifitter_1.0.1.tar.gz(r-4.6-any)
epifitter_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
epifitter/json (API)

# Install 'epifitter' in R:
install.packages('epifitter', repos = c('https://alvesks.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alvesks/epifitter/issues

Pkgdown/docs site:https://alvesks.github.io

Datasets:
  • PowderyMildew - Powdery mildew disease progress curves in organic tomato

On CRAN:

Conda:

6.78 score 6 stars 112 scripts 678 downloads 16 exports 72 dependencies

Last updated from:597ca59e2c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK145
source / vignettesOK283
linux-release-x86_64OK148
macos-release-arm64OK78
macos-oldrel-arm64OK83
windows-develOK92
windows-releaseOK79
windows-oldrelOK86
wasm-releaseOK121

Exports:AUDPCAUDPC_2_pointsAUDPSexpo_funfit_linfit_multifit_nlinfit_nlin2gompi_funlogi_funmono_funplot_fitsim_exponentialsim_gompertzsim_logisticsim_monomolecular

Dependencies:askpassbitbit64bootcellrangerclassclicliprcowplotcpp11crayoncurldata.tableDescToolsdeSolvedplyre1071Exactexpmfarverforcatsfsgenericsggplot2gldgluegtablehavenhmshttrisobandjsonlitelabelinglatticelifecyclelmommagrittrMASSMatrixmimeminpack.lmmvtnormopensslpillarpkgconfigprettyunitsprogressproxypurrrR6RColorBrewerRcppreadrreadxlrematchrlangrootSolverstudioapiS7scalesstringistringrsystibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr

Uncertainty bands for fitted disease progress curves
Why add intervals? | Example data | Residual bootstrap confidence bands | Wild bootstrap confidence bands | Compare multiple models | Nonlinear fits | Choosing a method
Rendered fromconfidence-intervals.Rmdusingknitr::rmarkdown

Last update: 2026-06-20
Started: 2026-06-20

Choosing and fitting disease progress models
Overview | Compare models quickly with fit_lin() | Nonlinear fitting with starting values | Weighted nonlinear fitting | Estimate K when the epidemic plateaus below 1 | Grouped fitting with fit_multi() | Practical reporting
Rendered frommodel-fitting.Rmdusingknitr::rmarkdown

Last update: 2026-06-20
Started: 2026-04-11

Real-data workflow: powdery mildew in organic tomato
Overview | Load the data | Inspect the experimental structure | Visualize treatment means over time | Calculate AUDPC and AUDPS per experimental unit | Fit a model to one observed curve | Fit models to many observed curves at once | Practical guidance | Data source
Rendered frompowdery-mildew-data.Rmdusingknitr::rmarkdown

Last update: 2026-06-20
Started: 2026-04-11

Simulation workflows for disease progress curves
Overview | Simulate four canonical curve shapes | Send simulated data into the fitting pipeline
Rendered fromsimulation-workflows.Rmdusingknitr::rmarkdown

Last update: 2026-06-20
Started: 2026-04-11

Start here: your first disease progress workflow
What epifitter does | A first epidemic | Fit candidate models | Visualize predictions | Work with multiple epidemics | Next steps
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-06-20
Started: 2026-04-11

Summarizing epidemics with AUDPC and AUDPS
Overview | Build an example curve | Compute AUDPC | Multiple observations at the same time | One summary curve versus one value per replicate | Compute AUDPS | Estimate AUDPC from two observations | Absolute versus relative measures | Practical guidance
Rendered fromarea-under-curve.Rmdusingknitr::rmarkdown

Last update: 2026-06-20
Started: 2026-04-11