Two-choice response times are a common type of data, and much research has been devoted to the development of process models for such data. However, the practical application of these models is notoriously complicated, and flexible methods are largely nonexistent. We combine a popular model for choice response times--the Wiener diffusion process--with techniques from psychometrics in order to construct a "hierarchical diffusion model". Chief among these techniques is the application of random effects, with which we allow for unexplained variability among participants, items, or other experimental units. These techniques lead to a modeling framework that is highly flexible and easy to work with. Among the many novel models this statistical framework provides are a "multilevel diffusion model," "regression diffusion models," and a large family of "explanatory diffusion models." We provide examples and the necessary computer code. (Contains 12 figures, 4 tables and 10 footnotes.)