A Comparison of R Tools for Nonlinear Least Squares Modeling
Published in R Journal, 2023
Abstract
Our Google Summer of Code project “Improvements to nls ()” investigated rationalizing R tools for nonlinear regression and nonlinear estimation tools by considering usability, maintainability, and functionality, especially for a Gauss-Newton solver. The rich features of nls () are weakened by several deficiencies and inconsistencies such as a lack of stabilization of the Gauss-Newton solver. Further considerations are the usability and maintainability of the code base that provides the functionality nls () claims to offer. Various packages, including our nlsr, provide alternative capabilities. We consider the differences in goals, approaches, and features of different tools for nonlinear least squares modeling in R. Discussion of these matters is relevant to improving R generally as well as its nonlinear estimation tools.
Recommended citation: Nash, J. C., & Bhattacharjee, A. (2023). A Comparison of R Tools for Nonlinear Least Squares Modeling. R Journal, 15(4). https://journal.r-project.org/articles/RJ-2023-091/RJ-2023-091.pdf