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nlsr: Functions for Nonlinear Least Squares Solutions - Updated 2022
Provides tools for working with nonlinear least squares problems. For the estimation of models reliable and robust tools than nls(), where the the Gauss-Newton method frequently stops with 'singular gradient' messages. This is accomplished by using, where possible, analytic derivatives to compute the matrix of derivatives and a stabilization of the solution of the estimation equations. Tools for approximate or externally supplied derivative matrices are included. Bounds and masks on parameters are handled properly.
Getting started
README.md
nlsr Background, Development, Examples and Discussion
nlsr Derivatives
nlsr Derivatives
nlsr Introduction
nlsr Introduction
Specifying Fixed Parameters
Specifying Fixed Parameters
Symbolic and analytical derivatives in R
An introduction to nlsr
nlsr and Other Approaches to Derivatives in R
nlsr/nlsr Background, Development, and Discussion
Specifying and Using Fixed Parameters in Nonlinear Estimation
Symbolic and analytical derivatives in R
Browse package contents
Package details |
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| Author | John C Nash [aut, cre], Duncan Murdoch [aut], Fernando Miguez [ctb], Arkajyoti Bhattacharjee [ctb] |
| Maintainer | John C Nash <nashjc@uottawa.ca> |
| License | GPL-2 |
| Version | 2023.8.31 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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Try the nlsr package in your browser
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