Generalized linear models (GLMs) also can accommodate outcomes that are not continuous and normally distributed. Indeed, one of the great advantages of GLMs is they provide a unified framework to understand regression models applied to variables assumed to come from a variety of distributions. For this chapter, we will lean heavily on one excellent R package, VGAM, which provides utilities for vector generalized linear models (VGLMs) and vector generalized additive models (VGAMs) [125]. VGLMs and VGAMs are an even more ...