Once a model has proven to provide a good description of the experimental data, it is possible to test several relevant hypotheses that can be “translated” into linear or nonlinear combinations of model parameters. This chapter presents examples of the relevance of such combinations and how they can be implemented in R, using several functions of interest, such as the glht() function in the multcomp package, the gnlht() function in the statforbiology package, and the emmeans() and contrast() functions in the emmeans package. Particular attention is given to least squares means, orthogonal contrasts, pairwise comparisons, back-transformations, predictions, and inverse predictions, which can all be defined in terms of linear or nonlinear combinations of the estimated model parameters.