Generalized linear models (GLMs), originally formulated by Nelder and Baker (2004), provide a unifying family of linear models that is widely used in practical regression analysis. The GLMs generalize ordinary linear regression by allowing the models to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Thus, these models allow for describing response variables that have an error distribution other than normal. They avoid having to select certain transformations of the data to achieve the possibly conflicting objects of normality, linearity and/or ho‐ mogeneity of variance. Commonly used GLMs include logistic regression for binary data and Poisson regression or negative‐binomial regression for count data.