Tutorial on generalised nonlinear models in R

In many practical applications we wish to model the expected value of a response that is non-Gaussian 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 non-Gaussian 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.