Package: epifitter 0.3.0

Kaique dos S. Alves

epifitter: Analysis and Simulation of Plant Disease Progress Curves

Analysis and visualization of plant disease progress curve data. Functions for fitting two-parameter population dynamics models (exponential, monomolecular, logistic and Gompertz) to proportion data for single or multiple epidemics using either linear or no-linear regression. Statistical and visual outputs are provided to aid in model selection. Synthetic curves can be simulated for any of the models given the parameters. See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058> for further information on the methods.

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

epifitter_0.3.0.tar.gz
epifitter_0.3.0.zip(r-4.7-any)epifitter_0.3.0.zip(r-4.6-any)epifitter_0.3.0.zip(r-4.5-any)
epifitter_0.3.0.tgz(r-4.6-any)epifitter_0.3.0.tgz(r-4.5-any)
epifitter_0.3.0.tar.gz(r-4.7-any)epifitter_0.3.0.tar.gz(r-4.6-any)
epifitter_0.3.0.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://epiverse-connect.r-universe.dev', 'https://cloud.r-project.org'))

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

Datasets:

On CRAN:

Conda:

5.89 score 7 stars 110 scripts 633 downloads 18 exports 76 dependencies

Last updated from:fa32823f50. Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develNOTE144
source / vignettesOK213
linux-releaseNOTE176
macos-releaseNOTE87
macos-oldrelNOTE112
windows-develNOTE65
windows-releaseNOTE84
windows-oldrelNOTE89
wasm-releaseOK113

Exports:AUDPCAUDPC_2_pointsAUDPSexpo_funfit_linfit_multifit_nlinfit_nlin2gompi_funlogi_funmono_funplot_fitprint.fit_linprint.fit_nlin2sim_exponentialsim_gompertzsim_logisticsim_monomolecular

Dependencies:askpassbitbit64bootcellrangerclassclicliprcpp11crayoncurldata.tableDescToolsdeSolvedplyre1071evaluateExactexpmfarverforcatsfsgenericsggplot2gldgluegtablehavenhighrhmshttrisobandjsonliteknitrlabelinglatticelifecyclelmommagrittrMASSMatrixmimeminpack.lmmvtnormopensslpillarpkgconfigprettyunitsprogressproxypurrrR6RColorBrewerRcppreadrreadxlrematchrlangrootSolverstudioapiS7scalesstringistringrsystibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrxfunyaml

Fitting and selecting models
Base functions | Basic usage | Dataset | Linear regression using fit_lin() | Model fit stats | Model coefficients | Global stats | Model predictions | Plot of predictions | Publication-ready plots | Non-linear regression | Two-parameters | Estimating K (maximum disease) | Fit models to multiple DPCs | Data | Using fit_multi() | Want to estimate K? | Graphical outputs | DPCs and fitted curves | Apparent infection rate | References
Rendered fromfitting.Rmdusingknitr::rmarkdown

Last update: 2021-06-01
Started: 2020-09-02

Simulating disease progress curves
Introduction | Hands On | Packages | The basics of the simulation | Exponential | Monomolecular | The Logistic model | Gompertz | Combo | References
Rendered fromsimulation.Rmdusingknitr::rmarkdown

Last update: 2021-06-01
Started: 2020-09-02