Type: Package
Title: Analysis and Simulation of Plant Disease Progress Curves
Version: 1.0.0
Description: 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.
Depends: R (≥ 4.1)
Imports: DescTools, cowplot, deSolve, dplyr, ggplot2, magrittr, minpack.lm, stats, tibble, tidyr
Suggests: knitr, rmarkdown, lemon, testthat (≥ 3.0.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
Date: 2026-04-11
URL: https://github.com/AlvesKS/epifitter, https://alvesks.github.io/epifitter/
BugReports: https://github.com/AlvesKS/epifitter/issues
X-schema.org-applicationCategory: Tools
Config/testthat/edition: 3
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2026-04-12 14:54:20 UTC; kai-q
Author: Kaique dos S. Alves ORCID iD [aut, cre], Emerson M. Del Ponte ORCID iD [aut], Adam H. Sparks ORCID iD [aut]
Maintainer: Kaique dos S. Alves <kaiquedsalves@gmail.com>
Repository: CRAN
Date/Publication: 2026-04-12 18:00:02 UTC

epifitter: Analysis and simulation of plant disease progress curves

Description

Internal package documentation used to generate the package namespace.

Author(s)

Maintainer: Kaique dos S. Alves kaiquedsalves@gmail.com (ORCID)

Authors:

See Also

Useful links:

Area under the disease progress curve

Description

Calculate the area under a disease progress curve using the trapezoidal method.

Usage

AUDPC(
  time,
  y,
  y_proportion = TRUE,
  type = "absolute",
  aggregate = c("mean", "median", "none")
)

Arguments

time

A numeric vector of assessment times.

y

A numeric vector of disease intensity values.

y_proportion

Logical. Are the 'y' values expressed as proportions?

type

Either '"absolute"' or '"relative"'.

aggregate

How to handle multiple observations at the same time point. The default, '"mean"', averages replicated observations before calculating area. '"median"' uses the median and '"none"' requires unique time values.

Value

A numeric scalar with the AUDPC value.

References

Madden, L. V., Hughes, G., and van den Bosch, F. (2007). The Study of Plant Disease Epidemics. American Phytopathological Society.

Examples

epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.5, n = 1)
AUDPC(time = epi$time, y = epi$y, y_proportion = TRUE)

Estimate AUDPC from two observations

Description

Estimate the area under the disease progress curve from only the initial and final observations under a logistic epidemic assumption.

Usage

AUDPC_2_points(time, y0, yT)

Arguments

time

Time elapsed between the two assessments.

y0

Initial disease intensity as a proportion.

yT

Final disease intensity as a proportion.

Value

A numeric scalar with the estimated AUDPC.

References

Jeger, M. J., and Viljanen-Rollinson, S. L. H. (2001). The use of the area under the disease-progress curve (AUDPC) to assess quantitative disease resistance in crop cultivars. Theoretical and Applied Genetics, 102, 32-40.

Examples

epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.5, n = 1)
AUDPC_2_points(time = epi$time[7], y0 = epi$y[1], yT = epi$y[7])

Area under the disease progress stairs

Description

Calculate the area under the disease progress stairs, an alternative to AUDPC that gives more balanced weight to the first and last observations.

Usage

AUDPS(
  time,
  y,
  y_proportion = TRUE,
  type = "absolute",
  aggregate = c("mean", "median", "none")
)

Arguments

time

A numeric vector of assessment times.

y

A numeric vector of disease intensity values.

y_proportion

Logical. Are the 'y' values expressed as proportions?

type

Either '"absolute"' or '"relative"'.

aggregate

How to handle multiple observations at the same time point. The default, '"mean"', averages replicated observations before calculating area. '"median"' uses the median and '"none"' requires unique time values.

Value

A numeric scalar with the AUDPS value.

References

Simko, I., and Piepho, H.-P. (2012). The area under the disease progress stairs: Calculation, advantage, and application. Phytopathology, 102, 381-389.

Examples

epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.5, n = 1)
AUDPS(time = epi$time, y = epi$y, y_proportion = TRUE)

Powdery mildew disease progress curves in organic tomato

Description

Experimental disease progress curve data for powdery mildew under different irrigation systems and soil moisture levels in organic tomato.

Format

A data frame with 240 rows and 5 variables:

irrigation_type

Irrigation system.

moisture

Soil moisture level.

block

Experimental block.

time

Assessment time.

sev

Disease severity as a proportion.

References

Lage, D. A. C., Marouelli, W. A., and Cafe-Filho, A. C. (2019). Management of powdery mildew and behaviour of late blight under different irrigation configurations in organic tomato. Crop Protection, 125, 104886.

Examples

data("PowderyMildew")
str(PowderyMildew)

Exponential model differential equation

Description

Internal helper used by the simulation functions to solve the exponential epidemic model with 'deSolve::ode()'.

Usage

expo_fun(t, y, par)

Arguments

t

Time.

y

State variable.

par

Model parameters.

Value

A list containing the rate of change.

Fit epidemic models using linearization

Description

Fit exponential, monomolecular, logistic, and Gompertz models to disease progress data using linearized forms of each model.

Usage

fit_lin(time, y)

Arguments

time

Numeric vector of assessment times.

y

Numeric vector of disease intensity values.

Value

A list with fit statistics, parameter estimates, and prediction data.

Examples

set.seed(1)
epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.2, n = 4)
fit_lin(time = epi$time, y = epi$random_y)

Fit models to multiple disease progress curves

Description

Apply 'fit_lin()', 'fit_nlin()', or 'fit_nlin2()' to multiple disease progress curves stored in a data frame.

Usage

fit_multi(
  time_col,
  intensity_col,
  data,
  strata_cols = NULL,
  starting_par = list(y0 = 0.01, r = 0.03, K = 0.8),
  maxiter = 500,
  nlin = FALSE,
  estimate_K = FALSE
)

Arguments

time_col

Character name specifying the time column.

intensity_col

Character name specifying the disease intensity column.

data

A data frame containing the variables for model fitting.

strata_cols

Character vector specifying grouping columns. Use 'NULL' to fit all rows as a single epidemic. Defaults to 'NULL'.

starting_par

Named list of starting values for model parameters.

maxiter

Maximum number of iterations for nonlinear fitting. Must be a positive number.

nlin

Logical. Should nonlinear fitting be used?

estimate_K

Logical. Should the asymptote 'K' be estimated?

Value

A list with grouped parameter estimates and prediction data.

Examples

set.seed(1)
epi1 <- sim_gompertz(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.2, n = 2)
epi2 <- sim_gompertz(N = 30, y0 = 0.01, dt = 5, r = 0.2, alpha = 0.2, n = 2)
data <- dplyr::bind_rows(epi1, epi2, .id = "curve")
fit_multi(time_col = "time", intensity_col = "random_y", data = data, strata_cols = "curve")
fit_multi(time_col = "time", intensity_col = "random_y", data = data)

Fit epidemic models using nonlinear regression

Description

Fit exponential, monomolecular, logistic, and Gompertz models to disease progress data using nonlinear regression.

Usage

fit_nlin(time, y, starting_par = list(y0 = 0.01, r = 0.03), maxiter = 50)

Arguments

time

Numeric vector of assessment times.

y

Numeric vector of disease intensity values.

starting_par

Named list with starting values for 'y0' and 'r'. When omitted or partially specified, 'epifitter' supplies data-driven fallback values.

maxiter

Maximum number of iterations. Must be a positive number.

Value

A list with fit statistics, parameter estimates, and prediction data.

Examples

set.seed(1)
epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.2, n = 4)
fit_nlin(time = epi$time, y = epi$random_y, starting_par = list(y0 = 0.01, r = 0.03))

Fit epidemic models and estimate the asymptote

Description

Fit monomolecular, logistic, and Gompertz epidemic models using nonlinear regression while also estimating the maximum disease intensity parameter 'K'.

Usage

fit_nlin2(
  time,
  y,
  starting_par = list(y0 = 0.01, r = 0.03, K = 0.8),
  maxiter = 50
)

Arguments

time

Numeric vector of assessment times.

y

Numeric vector of disease intensity values.

starting_par

Named list with starting values for 'y0', 'r', and 'K'. When omitted or partially specified, 'epifitter' supplies data-driven fallback values.

maxiter

Maximum number of iterations. Must be a positive number.

Value

A list with fit statistics, parameter estimates, and prediction data.

Examples

set.seed(1)
epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.5, n = 4)
fit_nlin2(
  time = epi$time,
  y = epi$random_y * 0.8,
  starting_par = list(y0 = 0.01, r = 0.1, K = 0.8),
  maxiter = 1024
)

Gompertz model differential equation

Description

Internal helper used by the simulation functions to solve the Gompertz epidemic model with 'deSolve::ode()'.

Usage

gompi_fun(t, y, par)

Arguments

t

Time.

y

State variable.

par

Model parameters.

Value

A list containing the rate of change.

Logistic model differential equation

Description

Internal helper used by the simulation functions to solve the logistic epidemic model with 'deSolve::ode()'.

Usage

logi_fun(t, y, par)

Arguments

t

Time.

y

State variable.

par

Model parameters.

Value

A list containing the rate of change.

Monomolecular model differential equation

Description

Internal helper used by the simulation functions to solve the monomolecular epidemic model with 'deSolve::ode()'.

Usage

mono_fun(t, y, par)

Arguments

t

Time.

y

State variable.

par

Model parameters.

Value

A list containing the rate of change.

Plot fitted epidemic models

Description

Create a faceted 'ggplot2' panel showing observed and fitted values for the selected epidemic models.

Usage

plot_fit(
  object,
  point_size = 1.2,
  line_size = 1,
  models = c("Exponential", "Monomolecular", "Logistic", "Gompertz")
)

Arguments

object

A fitted object returned by 'fit_lin()', 'fit_nlin()', or 'fit_nlin2()'.

point_size

Point size for observed values.

line_size

Line width for fitted curves.

models

Character vector with the models to display.

Value

A 'ggplot2' object.

Examples

epi <- sim_logistic(N = 30, y0 = 0.01, dt = 5, r = 0.3, alpha = 0.2, n = 4)
fit <- fit_lin(time = epi$time, y = epi$random_y)
plot_fit(fit)

Print fitted model summaries

Description

Print method for objects returned by [fit_lin()] and compatible fitters.

Usage

## S3 method for class 'fit_lin'
print(x, ...)

Arguments

x

An object produced by [fit_lin()] or [fit_nlin()].

...

Further arguments passed to [print()].

Print fitted model summaries with asymptote estimates

Description

Print method for objects returned by [fit_nlin2()].

Usage

## S3 method for class 'fit_nlin2'
print(x, ...)

Arguments

x

An object produced by [fit_nlin2()].

...

Further arguments passed to [print()].

Simulate an exponential disease progress curve

Description

Simulate disease progress data under the exponential epidemic model, with optional replicated observations.

Usage

sim_exponential(N = 10, dt = 1, y0 = 0.01, r, n, alpha = 0.2)

Arguments

N

Total epidemic duration.

dt

Time interval between assessments.

y0

Initial disease intensity.

r

Apparent infection rate.

n

Number of replicated curves.

alpha

Noise level applied to replicated observations.

Value

A data frame with simulated disease progress values and replicated noisy observations.

Examples

sim_exponential(N = 30, dt = 5, y0 = 0.01, r = 0.05, n = 4)

Simulate a Gompertz disease progress curve

Description

Simulate disease progress data under the Gompertz epidemic model, with optional replicated observations.

Usage

sim_gompertz(N = 10, dt = 1, y0 = 0.01, r, K = 1, n, alpha = 0.2)

Arguments

N

Total epidemic duration.

dt

Time interval between assessments.

y0

Initial disease intensity.

r

Apparent infection rate.

K

Maximum disease intensity.

n

Number of replicated curves.

alpha

Noise level applied to replicated observations.

Value

A data frame with simulated disease progress values and replicated noisy observations.

Examples

sim_gompertz(N = 30, dt = 5, y0 = 0.01, r = 0.05, K = 1, n = 4)

Simulate a logistic disease progress curve

Description

Simulate disease progress data under the logistic epidemic model, with optional replicated observations.

Usage

sim_logistic(N = 10, dt = 1, y0 = 0.01, r, K = 1, n, alpha = 0.2)

Arguments

N

Total epidemic duration.

dt

Time interval between assessments.

y0

Initial disease intensity.

r

Apparent infection rate.

K

Maximum disease intensity.

n

Number of replicated curves.

alpha

Noise level applied to replicated observations.

Value

A data frame with simulated disease progress values and replicated noisy observations.

Examples

sim_logistic(N = 30, dt = 5, y0 = 0.01, r = 0.05, K = 1, n = 4)

Simulate a monomolecular disease progress curve

Description

Simulate disease progress data under the monomolecular epidemic model, with optional replicated observations.

Usage

sim_monomolecular(N = 10, dt = 1, y0 = 0.01, r, K = 1, n, alpha = 0.2)

Arguments

N

Total epidemic duration.

dt

Time interval between assessments.

y0

Initial disease intensity.

r

Apparent infection rate.

K

Maximum disease intensity.

n

Number of replicated curves.

alpha

Noise level applied to replicated observations.

Value

A data frame with simulated disease progress values and replicated noisy observations.

Examples

sim_monomolecular(N = 30, dt = 5, y0 = 0.01, r = 0.05, K = 1, n = 4)