Planning and Conducting Simulation Studies in R (Part 2)

Half day short course (2:00pm – 5:00pm)

In the second part of this course, we will go through a potpourri of techniques for programming simulations in R more efficiently. In particular, we will discuss examples of what makes R code slow and fast, how to catch seldomly occurring errors through reproducibility, how to speed up simulations by means of parallelization and what needs to be taken into account for reproducible random number generation in R in general and particularly in parallelized code. In the end we will give a short outlook on other helpful work habits, such as using reproducible reports or Shiny Apps for presenting the results, how to work on Linux servers and why and how to use version control with git.

The course will consist of a mix of slide presentations and hands-on exercises in R. The participants bring their own laptops with R (and if they like RStudio) installed. During the exercises, the instructors will walk around and help the participants working on their own laptops.

Intended Audience

The intended audience of the second part of the course is undergraduate and graduate students, faculty and researchers who already have some experience with simulations in R (or have attended the first part of the course in the morning) and would like to learn or review a few helpful tricks.

About the instructors

Carolin Strobl

 Carolin Strobl is a professor for Psychological Methods at the University of Zurich, Switzerland, with a background in psychology and statistics. Her group has contributed to several software packages related to machine learning and psychometrics in the free, open source software R. She has conducted various simulation studies over the years and has extensive expertise in teaching statistics and machine learning using R across various academic levels. This year, Carolin and her team will also publish a textbook on conducting simulation studies in R.

Mirka Henninger

 Mirka Henninger is an assistant professor for Statistics & Data Science at the University of Basel, Switzerland. Her background is in psychology, and her research is grounded at the intersection of psychometrics, machine learning, and multilevel modelling. Herein, she has conducted many simulation studies, and has been teaching statistics together R as a software tool for data analysis and programming at various academic levels. Mirka is also part of the team around Carolin publishing a textbook on how to plan and realize simulation studies in R.