Tutorial: Introduction to Generalized Nonlinear Models in R 
AbstractIn many practical applications we wish to model the expected value of a response that is nonGaussian by a nonlinear function of covariates. Generalized nonlinear models provide a framework to do this. Generalized nonlinear models may be viewed as an extension to generalized linear models — allowing a nonlinear predictor — or an extension to nonlinear least squares — accommodating nonGaussian data. In this tutorial we shall provide an overview of generalized nonlinear models and describe how they may be specified, fitted and evaluated using the gnm package. We will also present several examples of how generalized nonlinear models are used in practice. 

AudiencePeople who wish to find out what generalized nonlinear models are and whether such models might be useful in their field of application, and people who want an introduction to the gnm package. 
Background KnowledgeIt will be assumed that participants have some familiarity with generalized linear models (e.g. logistic regression models, loglinear models, etc) and/or nonlinear least squares models. 
SlidesYou can download the slides of the course 