## Learning outcomes

After completing a course you will able to:

- Simulate linear, nonlinear and coupled dynamics using simple models.
- Conduct (multi-fractal) Detrended Fluctuation Analysis and related techniques to quantify global and local scaling relations.
- Conduct Recurrence Quantification Analysis and related techniques to quantify temporal patterns, synchronisation and coupling direction.
- Conduct analyses on (multiplex) Recurrence Networks to quantify structure and dynamics of (multivariate) time series.

Naturally the (depth of) topics discussed will be limited by the duration of the course.

### For whom are these courses designed?

The courses are designed for all researchers who are interested in acquiring hands-on experience with applying research methods and analytic techniques to study human behaviour from the perspective of Complexity Science. Prior knowledge is not required, some experience using R is recommended.

### Admission requirements

During the course we will mostly be using the R statistical software environment. Basic experience with R is highly recommended (e.g. installing packages, calling functions that run analyses, handling and plotting data). We also offer a module for the Jamovi software with which the most basic analyses can be conducted. Using Jamovi does not require any prior knowledge of R, but you will not be able to use more advanced features of certain analyses.

Please bring your own laptop to the course. We will help you to install the necessary open source software, all of which can run on Windows, MacOS and most likely also on common varieties of Unix/Linux. The specifications for your computer are simply this: You need to be able to connect to a wireless network (wifi) and you should be able to install and run R (https://www.r-project.org). In addition, you might want to be able to use RStudio (https://www.rstudio.com) and Jamovi (https://www.jamovi.org).

If you do not have the resources to bring a laptop that meets the required specifications, please let us know in advance so we can try to find an alternative solution.