Martijn Wieling, University of Groningen

This course will focus on introducing generalized additive models (GAMs) to analyze data in linguistics. Generalized additive modeling is an extension of linear mixed-effects regression, in that it allows one to fit non-linear patterns in addition to taking into account the structural variability associated with (e.g.,) participants and items. In three lectures, focusing on various types of data (i.e. articulatory, EEG, and language variation data), we will see how to model one-dimensional, two-dimensional and multi-dimensional patterns. Each lecture also contains a hands-on lab session, but instead it is also possible to work on one’s own data.

Experience in fitting regression and mixed-effects regression models in R is required.
It will be very helpful if people taking the course have read through this paper:
https://www.sciencedirect.com/science/article/abs/pii/S0095447017301377