Nonlinear relationships abound in nature, though typical statistical models ignore this in favor of simplicity, often at a cost of both predictive capabilities and better understanding of the underlying phenomenon of interest.  One means to explore such relationships is through generalized additive models (GAM).

This workshop will introduce participants to GAMs as a means to extend their efforts beyond the usual GLM setting.  In addition, extensions and connections to other models will be noted (e.g. mixed and spatial).  Demonstration will be conducted with R, and the mgcv package in particular.

Link: https://m-clark.github.io/generalized-additive-models/