Package: nlmrt
Type: Package
Title: Functions for Nonlinear Least Squares Solutions
Version: 2016.3.2
Date: 2016-3-2
Authors@R: c( person(given = c("John", "C."), family = "Nash", role =
c("aut", "cre"), email = "nashjc@uottawa.ca") )
Author: John C. Nash [aut, cre]
Maintainer: John C. Nash <nashjc@uottawa.ca>
Description: 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.
License: GPL-2
Depends: R (>= 2.15.0)
Suggests: minpack.lm, optimx, Rvmmin, Rcgmin, numDeriv
NeedsCompilation: no
Packaged: 2016-03-04 16:30:35 UTC; john
Repository: CRAN
Date/Publication: 2016-03-04 23:57:39

Title: Nonlinear Network Reconstruction and Clustering Based on DCOL
(Distance Based on Conditional Ordered List)
Description: It includes three methods: K-profiles clustering, non-linear network reconstruction, and non-linear hierarchical clustering.
Imports: TSP, igraph, fdrtool, coin, methods, graphics, stats
Packaged: 2015-10-13 10:41:53 UTC; liuhaodong1

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

Title: Nonlinear Network, Clustering, and Variable Selection Based on
Description: 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>.