  Stan is an open-source probabilistic programing language, primarily designed
to do Bayesian data analysis. Its main inference algorithm is an adaptive
Hamiltonian Monte Carlo sampler, supported by state of the art gradient
computation. Stan's strengths include efficient computation, an expressive
language which offers a great deal of flexibility, and numerous diagnostics
that allow modelers to check whether the inference is reliable. Torsten extends
Stan with a suite of functions that facilitate the specification of
pharmacokinetic and pharmacodynamic models, and makes it straightforward to
specify a clinical event schedule. Part I of this tutorial demonstrates how to
build, fit, and criticize standard pharmacokinetic and pharmacodynamic models
using Stan and Torsten.
