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: | 2022.8.16 |
| Depends: | R (≥ 3.5) |
| Imports: | digest |
| Suggests: | minpack.lm, optimx, numDeriv, knitr, rmarkdown, markdown, Ryacas, Deriv, microbenchmark, MASS, ggplot2, nlraa |
| Published: | 2022-08-16 |
| Author: | John C Nash [aut, cre], Duncan Murdoch [aut] |
| Maintainer: | John C Nash <nashjc at uottawa.ca> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | Optimization |
| CRAN checks: | nlsr results |
| Reference manual: | nlsr.pdf |
| Vignettes: |
Specifying Fixed Parameters Symbolic and analytical derivatives in R nlsr Derivatives nlsr Background, Development, Examples and Discussion |
| Package source: | nlsr_2022.8.16.tar.gz |
| Windows binaries: | r-devel: nlsr_2022.8.16.zip, r-release: nlsr_2022.8.16.zip, r-oldrel: nlsr_2022.8.16.zip |
| macOS binaries: | r-release (arm64): nlsr_2022.8.16.tgz, r-oldrel (arm64): nlsr_2022.8.16.tgz, r-release (x86_64): nlsr_2022.8.16.tgz, r-oldrel (x86_64): nlsr_2022.8.16.tgz |
| Old sources: | nlsr archive |
| Reverse depends: | colf |
| Reverse imports: | genSEIR, usl |
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