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.

Version: 2023.8.31
Depends: R (≥ 3.5)
Imports: digest
Suggests: minpack.lm, optimx, numDeriv, knitr, rmarkdown, markdown, Ryacas, Deriv, microbenchmark, MASS, ggplot2, nlraa
Published: 2023-09-05
DOI: 10.32614/CRAN.package.nlsr
Author: John C Nash [aut, cre], Duncan Murdoch [aut], Fernando Miguez [ctb], Arkajyoti Bhattacharjee [ctb]
Maintainer: John C Nash <nashjc at uottawa.ca>
License: GPL-2
NeedsCompilation: no
Materials: README NEWS
In views: Optimization
CRAN checks: nlsr results

Documentation:

Reference manual: nlsr.pdf
Vignettes: Specifying Fixed Parameters
nlsr Introduction
Symbolic and analytical derivatives in R
nlsr Derivatives
nlsr Background, Development, Examples and Discussion

Downloads:

Package source: nlsr_2023.8.31.tar.gz
Windows binaries: r-devel: nlsr_2023.8.31.zip, r-release: nlsr_2023.8.31.zip, r-oldrel: nlsr_2023.8.31.zip
macOS binaries: r-release (arm64): nlsr_2023.8.31.tgz, r-oldrel (arm64): nlsr_2023.8.31.tgz, r-release (x86_64): nlsr_2023.8.31.tgz, r-oldrel (x86_64): nlsr_2023.8.31.tgz
Old sources: nlsr archive

Reverse dependencies:

Reverse depends: colf
Reverse imports: beezdemand, genSEIR, usl

Linking:

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