nlmrt: Functions for Nonlinear Least Squares Solutions
Replacement for nls() tools for working with nonlinear least squares problems.
The calling structure is 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.
||R (≥ 2.15.0)
||minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv
||John C. Nash [aut, cre]
||John C. Nash <nashjc at uottawa.ca>
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