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.

Nonlinear Network Reconstruction and Clustering Based on DCOL
(Distance Based on Conditional Ordered List)
It includes three methods: K-profiles clustering, non-linear network reconstruction, and non-linear hierarchical clustering.
TSP, igraph, fdrtool, coin, methods, graphics, stats

Nonlinear Network Reconstruction, Clustering, and Variable
Selection Based on DCOL (Distance Based on Conditional Ordered
List)
It includes four methods: DCOL-based K-profiles clustering, non-linear network reconstruction, non-linear hierarchical clustering, and variable selection for generalized additive model.
ROCR, TSP, igraph, fdrtool, coin, methods, graphics, stats,
earth, randomForest, e1071

Nonlinear Network, Clustering, and Variable Selection Based on
DCOL
It includes four methods: DCOL-based K-profiles clustering, non-linear network reconstruction, non-linear hierarchical clustering, and variable selection for generalized additive model. References:  Tianwei Yu (2018)<DOI: 10.1002/sam.11381>; Haodong Liu and others (2016)<DOI: 10.1371/journal.pone.0158247>; Kai Wang and others (2015)<DOI: 10.1155/2015/918954>; Tianwei Yu and others (2010)<DOI: 10.1109/TCBB.2010.73>.

nloptr is an R interface to NLopt. NLopt is a
free/open-source library for nonlinear optimization, providing
a common interface for a number of different free optimization
routines available online as well as original implementations
of various other algorithms. See
http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction for
more information on the available algorithms.