The goal of biogrowth is to ease the development of mathematical models to describe population growth. It includes functions for:

- making predictions under static environmental conditions.
- making predictions under dynamic environmental conditions.
- making predictions under static or dynamic conditions considering parameter uncertainty.
- fitting models to data gathered under static environmental conditions.
- fitting models to data gathered under dynamic environmental conditions.

The fuctions in biogrowth follow the methods of predictive microbiology, where the modelling process is divided two steps: primary and secondary modelling. The user has the flexibility to choose between several primary (Baranyi, modified Gompertz and Trilinear) and secondary models (cardinal parameter model, Zwietering-type model, full Ratkowsky model).

The biogrowth package has been developed by researchers of the Food Microbiology Laboratory of Wageningen University and Research.

- Alberto Garre,
- Jeroen Koomen,
- Heidy den Besten,
- Marcel Zwietering.

Questions and comments can be directed to Alberto Garre (alberto.garreperez (at) wur.nl). For bug reports, please use the GitHub page of the project.

You can install the released version of biogrowth from CRAN with:

```
install.packages("biogrowth")
```

And the development version from GitHub with:

```
# install.packages("devtools")
devtools::install_github("albgarre/biogrowth")
```

As an example of the features included in the package, the following code chunk generates a prediction of microbial growth under dynamic conditions considering parameter uncertainty.

```
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
#> ✓ tibble 3.0.4 ✓ dplyr 1.0.2
#> ✓ tidyr 1.1.2 ✓ stringr 1.4.0
#> ✓ readr 1.4.0 ✓ forcats 0.5.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
library(biogrowth)
set.seed(1241)
my_model <- "Baranyi"
my_times <- seq(0, 30, length = 100)
n_sims <- 3000
pars <- tribble(
~par, ~mean, ~sd, ~scale,
"logN0", 0, .2, "original",
"mu", 2, .3, "sqrt",
"lambda", 4, .4, "sqrt",
"logNmax", 6, .5, "original"
)
stoc_growth <- predict_stochastic_growth(my_model, my_times, n_sims, pars)
plot(stoc_growth)
```

As an additional example, the following code chunk fits a model to a set of experiments under dynamic conditions.

```
## We will use the multiple_experiments data set
data("multiple_experiments")
## For each environmental factor, we need to defined a model
sec_names <- c(temperature = "CPM", pH = "CPM")
## Any model parameter can be fixed
known <- list(Nmax = 1e8, N0 = 1e0, Q0 = 1e-3,
temperature_n = 2, temperature_xmin = 20, temperature_xmax = 35,
pH_n = 2, pH_xmin = 5.5, pH_xmax = 7.5, pH_xopt = 6.5)
## The rest require starting values for model fitting
start <- list(mu_opt = .8, temperature_xopt = 30)
## We can now call the fitting function
global_fit <- fit_multiple_growth(start, multiple_experiments, known, sec_names)
## Any single environmental factor can be added to the plot using add_factor
plot(global_fit, add_factor = "temperature")
```

This is only a small sample of the functions included in the package. For a complete list, please check the package vignette.

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