Nonparametric shape constraints offer statisticians the potential of freedom from restrictive parametric assumptions, while still permitting fully automatic procedures.  In this sense, they combine the best of both the parametric and nonparametric worlds. Inference under shape constraints is a core area of statistical theory and methodology, but the methods often involve challenging optimisation problems and have important applications in many areas, including biology and econometrics.