Linear regression is a ubiquitous approach for analyzing relationships between variables, but it assumes that those relationships are linear, which may not always be the case with real-world data. Generalized additive models (GAMs) are a straightforward and flexible tool that extend linear and generalized linear models to allow non-nonlinear relationships. This workshop provides an overview of the theory and intuition behind GAMs and demonstrates their practical use through worked examples in R using the mgcv package.