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.

References in zbMATH (referenced in 2 articles )

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  1. John Nash: On Best Practice Optimization Methods in R (2014) not zbMATH
  2. Nash, John C.: Nonlinear parameter optimization using R tools (2014)