  Shape-constrained nonparametric regression is a growing area in econometrics,
statistics, operations research, machine learning and related fields. In the
field of productivity and efficiency analysis, recent developments in the
multivariate convex regression and related techniques such as convex quantile
regression and convex expectile regression have bridged the long-standing gap
between the conventional deterministic-nonparametric and stochastic-parametric
methods. Unfortunately, the heavy computational burden and the lack of
powerful, reliable, and fully open access computational package has slowed down
the diffusion of these advanced estimation techniques to the empirical
practice. The purpose of the Python package pyStoNED is to address this
challenge by providing a freely available and user-friendly tool for the
multivariate convex regression, convex quantile regression, convex expectile
regression, isotonic regression, stochastic nonparametric envelopment of data,
and related methods. This paper presents a tutorial of the pyStoNED package and
illustrates its application, focusing on the estimation of frontier cost and
production functions.
