Package: gsl
Version: 1.9-3
Date: 16-09-2007
Title: wrapper for the Gnu Scientific Library
Author: Robin K. S. Hankin; qrng functions by Duncan Murdoch and
multimin by Andrew Clausen; spline functions by Jeffrey Racine
SystemRequirements: Gnu Scientific Library version >= 1.12
Description: An R wrapper for the special functions and quasi random
number generators of the Gnu Scientific Library
(http://www.gnu.org/software/gsl/).  See gsl-package.Rd for
details of overall package organization, and Misc.Rd for some
functions that are widely used in the package, and some tips on
installation.
Maintainer: Robin K. S. Hankin <hankin.robin@gmail.com>
License: GPL-2
Packaged: 2010-04-06 14:46:08 UTC; rksh
Repository: CRAN
Date/Publication: 2010-04-06 17:12:57

An R wrapper for some of the functionality of the

Authors@R: c(person(given=c("Robin", "K. S."), family="Hankin", role = c("aut","cre"), email="hankin.robin@gmail.com", comment = c(ORCID = "0000-0001-5982-0415")),
Author: Robin K. S. Hankin [aut, cre] (<https://orcid.org/0000-0001-5982-0415>),
Andrew Clausen [ctb] (multimin functionality),

Title: GSL Nonlinear Least-Squares Fitting
Authors@R: person("Joris", "Chau", email = "joris.chau@openanalytics.eu", role = c("aut", "cre"))
Description: An R interface to nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquadt algorithm with and without geodesic acceleration, and several variants of Powell's dogleg algorithm. Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.

Description: An R interface to nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquadt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.