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Shape-constrained nonparametric regression is a growing area in econometrics,statistics, operations research, machine learning and related fields. In thefield of productivity and efficiency analysis, recent developments in themultivariate convex regression and related techniques such as convex quantileregression and convex expectile regression have bridged the long-standing gapbetween the conventional deterministic-nonparametric and stochastic-parametricmethods. Unfortunately, the heavy computational burden and the lack ofpowerful, reliable, and fully open access computational package has slowed downthe diffusion of these advanced estimation techniques to the empiricalpractice. The purpose of the Python package pyStoNED is to address thischallenge by providing a freely available and user-friendly tool for themultivariate convex regression, convex quantile regression, convex expectileregression, isotonic regression, stochastic nonparametric envelopment of data,and related methods. This paper presents a tutorial of the pyStoNED package andillustrates its application, focusing on the estimation of frontier cost andproduction functions.

pyStoNED: A Python Package for Convex Regression and Frontier Estimation