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Table 1 User-level functions available in epifitter, a R package that provides a suite for fitting models and simulating disease progress curves (DPCs) and calculating areas under disease progress curves and stairs

From: Analysis and simulation of plant disease progress curves in R: introducing the epifitter package

Function Description
fit_lin() Fits models to single DPC data via linearization
fit_nlin() Fits models to single DPC via nonlinear regression
fit_nlin2() An extension of fit_nlin() that allows estimating the maximum asymptote K parameter
fit_multi() Fits models to multiple DPCs using either linear and nonlinear regression
plot_fit() Generates ggplot2 visualization of output of a model fitting object via fit_lin(), fit_nlin(), and fit_nlin2()
sim_exponential() Simulates DPC using the exponential model
sim_monomolecular() Simulates DPC using the monomolecular model
sim_logistic() Simulate DPC using the logistic model
sim_gompertz() Simulate DPC using the Gompertz model
AUDPC() Calculates the area under the disease progress curves
AUDPS() Calculates the area under the disease progress stairs
PowderyMildew Dataset containing experimental data of disease progress curves of powdery mildew under different irrigation systems and soil moisture levels in organic tomato (Lage et al. 2019)