```
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
#> ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
#> ✓ tibble 3.1.6 ✓ dplyr 1.0.8
#> ✓ tidyr 1.2.0 ✓ stringr 1.4.0
#> ✓ readr 2.1.2 ✓ forcats 0.5.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
```

The goal of biogrowth is to ease the development of mathematical models to describe population growth. It includes functions for making growth predictions and for fitting growth models. The growth predictions can account for

- constant environmental conditions (using only primary growth models)
- dynamic environmental conditions (accounting for their effect on the specific growth rate using secondary growth models)
- uncertainty in the model parameters

The package includes different approaches for model fitting:

- fitting primary models
- fitting both primary and secondary models to data gathered under dynamic environmental conditions
- fitting both primary and secondary models to several experiments with different environmental conditions (global fitting)
- fitting secondary models to a dataset of growth rates obtained under different conditions

In all these cases, the package allows choosing which model parameters to estimate from the data and which ones to fix to known values.

Besides this, the package includes several additional functions to ease in the interpretation of the calculations. This includes:

- functions for model comparison
- functions to obtain a statistical summary of the fit
- functions to ease visualization of model predictions and model fits
- functions to calculate the time to reach a given population size
- functions to facilitate the definition of initial guesses for the model parameters

The approach of **biogrowth** is based on the methods of predictive
microbiology. Nonetheless, its equations should be applicable to other
fields. It implements several mathematical models, giving the user the
possibility to select among them.

The biogrowth package has been developed by researchers of the Food Microbiology Laboratory of Wageningen University and Research (the Netherlands) and the Universidad Politecnica de Cartagena (Spain).

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

Questions and comments can be directed to Alberto Garre (alberto.garre (at) upct.es). 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")
```

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

```
## We will use the data on growth of Salmonella included in the package
data("growth_salmonella")
## We will fit 3 different models to the data
fit1 <- fit_growth(growth_salmonella,
list(primary = "Baranyi"),
start = c(lambda = 0, logNmax = 8, mu = .1, logN0 = 2),
known = c(),
environment = "constant",
)
fit2 <- fit_growth(growth_salmonella,
list(primary = "Baranyi"),
start = c(logNmax = 8, mu = .1, logN0 = 2),
known = c(lambda = 0),
environment = "constant",
)
fit3 <- fit_growth(growth_salmonella,
list(primary = "modGompertz"),
start = c(C = 8, mu = .1, logN0 = 2),
known = c(lambda = 0),
environment = "constant",
)
## We can now put them in a (preferably named) list
my_models <- list(`Baranyi` = fit1,
`Baranyi no lag` = fit2,
`Gompertz no lag` = fit3)
## And pass them to compare_growth_fits
model_comparison <- compare_growth_fits(my_models)
## The instance of GrowthComparison has useful S3 methods
print(model_comparison)
#> Comparison between models fitted to data under isothermal conditions
#>
#> Statistical indexes arranged by AIC:
#>
#> # A tibble: 3 × 5
#> model AIC df ME RMSE
#> <chr> <dbl> <int> <dbl> <dbl>
#> 1 Baranyi -29.7 17 0.000000402 0.0919
#> 2 Baranyi no lag 4.49 18 -0.000000513 0.224
#> 3 Gompertz no lag 7.47 18 0.000000148 0.241
plot(model_comparison)
```

```
plot(model_comparison, type = 2)
```

```
plot(model_comparison, type = 3)
#> `geom_smooth()` using formula 'y ~ x'
```

```
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"
)
unc_growth <- predict_growth_uncertainty(my_model, my_times, n_sims, pars)
plot(unc_growth)
```

```
## We will use the data included in the package
data("multiple_counts")
data("multiple_conditions")
## We need to assign a model equation for each environmental factor
sec_models <- list(temperature = "CPM", pH = "CPM")
## Any model parameter (of the primary or secondary models) can be fixed
known_pars <- 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, need initial guesses
my_start <- list(mu_opt = .8, temperature_xopt = 30)
## We can now fit the model
global_fit <- fit_growth(multiple_counts,
sec_models,
my_start,
known_pars,
environment = "dynamic",
algorithm = "regression",
approach = "global",
env_conditions = multiple_conditions
)
## The instance of FitMultipleDynamicGrowth has nice S3 methods
plot(global_fit)
```

```
summary(global_fit)
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> mu_opt 0.54196 0.01222 44.35 <2e-16 ***
#> temperature_xopt 30.62396 0.18728 163.52 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.4282 on 48 degrees of freedom
#>
#> Parameter correlation:
#> mu_opt temperature_xopt
#> mu_opt 1.0000 0.8837
#> temperature_xopt 0.8837 1.0000
```

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