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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