Provides tools for working with nonlinear least squares problems. It is intended to eventually supersede the nls() function in the R distribution. For example, nls() specifically does NOT deal with small or zero residual problems. Its Gauss-Newton method frequently stops with 'singular gradient' messages.
| Version: | 2017.6.18 |
| Depends: | R (≥ 3.0) |
| Imports: | digest |
| Suggests: | minpack.lm, optimr, Rvmmin, Rcgmin, numDeriv, knitr, rmarkdown, Ryacas, Deriv, nlmrt |
| Published: | 2017-06-19 |
| Author: | John C Nash [aut, cre], Duncan Murdoch [aut] |
| Maintainer: | John C Nash <nashjc at uottawa.ca> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | nlsr results |
| Reference manual: | nlsr.pdf |
| Vignettes: |
Specifying Fixed Parameters nlsr Derivatives nlsr Background, Development, Examples and Discussion |
| Package source: | nlsr_2017.6.18.tar.gz |
| Windows binaries: | r-devel: nlsr_2017.6.18.zip, r-release: nlsr_2017.6.18.zip, r-oldrel: nlsr_2017.6.18.zip |
| OS X El Capitan binaries: | r-release: nlsr_2017.6.18.tgz |
| OS X Mavericks binaries: | r-oldrel: nlsr_2017.6.18.tgz |
| Old sources: | nlsr archive |
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