Department of
Environmental Sciences, Informatics and Statistics

Summer School GAMs with R 
Introduction to modern Generalised Additive Models in R

  • When: 23-24 June 2025
  • Where: Ca’ Foscari University,  Scientific Campus (Aula Zeta A), Via Torino 155, Venice Mestre
  • Professor: Simon Wood
    Simon Wood is Chair of Computational Statistics in the School of Mathematics, University of Edinburgh. He is the author of "Generalized Additive Models: An Introduction with R" and the R package mgcv for generalized additive and other smooth regression models.

Overview

Generalized Additive Models (GAMs) are an extension of traditional regression models and have proved to be highly useful for both predictive and inferential purposes in a variety of scientific and commercial applications. One reason behind the popularity of GAMs is that they strike a balance between flexibility and interpretability, while being able to handle large data sets.

The taught part of the course will provide an overview of GAM theory, methods and software, while the hands-on sessions will make sure that the attendees will be ready to start doing GAM modelling in R as soon as the course is over.

Topics covered
  • Basics of basis penalty smoothing
  • GAMs
  • Empirical Bayes framework for inference and smoothness selection
  • Cross-validation approaches
  • Mixed GAMs, distributional regression and other extensions
  • The mgcv package
  • Practical model specification, checking and visualization
  • Model building and further inference. 
Learning outcomes
  • Understand the basic theory underpinning regression modelling with spline like smoothers
  • Use mgcv to fit various GAMs to data, including mixed model extensions, GAMs for location scale and shape (distributional regression), and some functional data analysis
  • Be able to check modelling assumptions
  • Compare and critique competing models
  • Justify your modelling choices

Learning methods

The course delivery is based on both lectures covering the theory and practical hand-on sessions with R and mgcv. 

Who is it aimed at

Target audience

Anyone with some statistics training who is aware of the advantages of nonlinear modelling could benefit from attending.
Fields where this may be most popular are: science, insurance, finance, public health, epidemiology, psychology, econometrics.

Assumed knowledge

Attendees should be comfortable with using R. They should understand generalised linear models, though this can be intuitive and doesn’t have to be mathematically rigorous.
They do not need to have used mgcv before.

How to apply

Don't miss the opportunity to learn from the top experts in the field: sign up for the Summer School.
Applications will open in February 2025; more details will be published soon.

For information write to  dais.eccellente@unive.it.

Last update: 12/02/2025