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 GaussNewton method within nls(). The MarquardtNash 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.
Package details 


Author  John C. Nash [aut, cre] 
Date of publication  20160304 23:57:39 
Maintainer  John C. Nash <[email protected]> 
License  GPL2 
Version  2016.3.2 
Package repository  View on CRAN 
Installation 
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