nlmrt: Functions for Nonlinear Least Squares Solutions
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 solution
may be one of a set of possibilities, and may also be statistically unsatisfactory.
Because of the intended aggressive policy to find solutions, the approach may
use additional and unnecessary function and Jacobian evaluations.
Note that the Jacobian function is developed using analytic expressions rather
than numerical approximations if this is possible.
Added print and summary as of August 28, 2012.