install.Rmd

---
title: "Install bupaR"
---



```{r echo = F, out.width="25%", fig.align = "right"}
knitr::include_graphics("images/icons/install.PNG")
```

***

# Installing bupaR

The easiest way to install the core **bupaR** packages is by installing the `bupaverse`-package. 

```{r eval = F}
install.packages("bupaverse")
```

You can then load the packages using `library()`. 

```{r eval = F}
library(bupaverse)
```

This allows you to use `bupaR`, `eventdataR`, `edeaR`, `processcheckR`, and `processmapR`. 

The following packages can be installed from [CRAN](https://cran.r-project.org/) individually using `install.packages()`.

* `daqapo`
* `heuristicsmineR`
* `petrinetR`
* `pm4py`
* `processanimateR`
* `processpredictR`
* `psmineR`
* `understandBPMN`
* `xesreadR`

Dev-versions of these packages can be installed from [GitHub](https://github.com/bupaverse) using `remotes::install_github()`.

In the same way, the following experimental packages can be installed.  

* `collaborateR`
* `bpmnR`
* `propro`

## Additional requirements

### `processpredictR`

Using the `processpredictR` package requires a Python installation on your machine. Check the [Tensorflow documentation](https://tensorflow.rstudio.com/install/) to get started. Once `tensorflow` and `keras` are installed, `processpredictR` will be ready to be used.