Package: nlmrt
Type: Package
Title: Functions for nonlinear least squares solutions
Version: 2012-8.11
Date: 2012-08-11
Author: John C. Nash
Maintainer: <nashjc@uottawa.ca>
Description: nlmrt provides tools for working with nonlinear least
squares problems using a calling structure similar to, but much
simpler than, that of the nls() function. Moreover, where nls()
specifically does NOT deal with small or zero residual
problems, nlmrt is quite happy to solve them. It also attempts
to be more robust in finding solutions, thereby avoiding
'singular gradient' messages that arise in the Gauss-Newton
method within nls(). The Marquardt-Nash approach in nlmrt
generally works more reliably to get a solution, though this
may be one of a set of possibilities, and may also be
statistically unsatisfactory.
License: GPL-2
Depends: minpack.lm, optimx, Rvmmin, Rcgmin
Packaged: 2012-08-11 21:46:30 UTC; john
Repository: CRAN
Date/Publication: 2012-08-12 15:24:51

Package: nlmrt
Type: Package
Title: Functions for nonlinear least squares solutions
Version: 2012-8.12
Date: 2012-08-12
Author: John C. Nash
Maintainer: <nashjc@uottawa.ca>
Description: nlmrt provides tools for working with nonlinear least
squares problems using a calling structure similar to, but much
simpler than, that of the nls() function. Moreover, where nls()
specifically does NOT deal with small or zero residual
problems, nlmrt is quite happy to solve them. It also attempts
to be more robust in finding solutions, thereby avoiding
'singular gradient' messages that arise in the Gauss-Newton
method within nls(). The Marquardt-Nash approach in nlmrt
generally works more reliably to get a solution, though this
may be one of a set of possibilities, and may also be
statistically unsatisfactory.
License: GPL-2
Depends: R (>= 2.15.0), minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv
Packaged: 2012-08-13 15:01:04 UTC; john
Repository: CRAN
Date/Publication: 2012-08-13 16:49:15

Package: nlmrt
Type: Package
Title: Functions for nonlinear least squares solutions
Version: 2013-8.10
Date: 2013-08-10
Authors@R: c( person(given = c("John", "C."), family = "Nash", role =
c("aut", "cre"), email = "nashjc@uottawa.ca"))
Maintainer: John C. Nash <nashjc@uottawa.ca>
Description: nlmrt provides tools for working with nonlinear least squares problems
using a calling structure similar to, but much simpler than, that of the nls()
function. Moreover, where nls() specifically does NOT deal with small or zero
residual problems, nlmrt is quite happy to solve them. It also attempts to be
more robust in finding solutions, thereby avoiding 'singular gradient' messages
that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach
in nlmrt generally works more reliably to get a solution, though this may be
one of a set of possibilities, and may also be statistically unsatisfactory.
Added print and summary as of August 28, 2012.
License: GPL-2
Depends: R (>= 2.15.0)
Suggests: minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv
Packaged: 2013-08-11 16:42:17 UTC; john
Author: John C. Nash [aut, cre]
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-08-11 18:56:49