Many models that we build rely on combining several components, often representing different processes at work in the system. For example in ecology we may have a component for detectability of the species by observers, one for whether the animals are available, one describing their spatial distribution.
Defining such a complex model in R can result in writing a very long call to a particular function, which may or may not work at any point. Since these processes are often (conditionally) independent, we can separate these components in the likelihood and code. Using the + operator we can then combine them in a simple way that's easy for those doing the modelling to understand. Since each step produces an object, we can perform model validation, diagnostics and checking during construction.
Through an example applied to distance sampling, I'll show an example of this kind of approach to model building and propose some other situations where this kind of strategy may also be useful. I hope this will provoke some debate on the user friendliness of modelling packages and their interfaces.