README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# biogrowth <img src="man/figures/logo.png" align="right" width="120" />

```{r, echo=FALSE}
library(badger)
```


<!-- badges: start -->
`r badge_cran_checks("badger")`
`r badge_cran_release("biogrowth", "green")`
`r badge_cran_download("biogrowth", "last-month", "green")`
`r badge_lifecycle("maturing", "blue")`
<!-- badges: end -->

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).

## Authors

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.


## Installation

You can install the released version of biogrowth from [CRAN](https://CRAN.R-project.org) with:

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

And the development version from [GitHub](https://github.com/) with:

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

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.

```{r example}
library(tidyverse)
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. 

```{r}

## 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.