An R library for nonlinear black-box system identification

The Nonlinear AutoRegressive with eXogenous inputs (NARMAX) models are among the most general classes of mathematical abstractions for dynamic systems and has many successful applications os data-driven modeling in different fields. In the present paper we introduce the narmax package in R for nonlinear black-box system identification using power-form polynomials. The goals are to provide the community with software which enables the resolution of nonlinear identification problems effectively, so practitioners can share their code with repeatable results, and to introduce a framework so one can build on to provide other identification methods in the rich R ecosystem. We aim at encapsulating procedures such as generating regression matrices, predicting free-run simulation, estimation of the parameters with standard and extended orthogonal least squares methods, and model validation utilities, so the user can focus most of the time and effort on building and testing different models. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).