marqLevAlg: A Parallelized Algorithm for Least-Squares Curve Fitting

This algorithm provides a numerical solution to the problem of minimizing (or maximizing) a function. This is more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). A new convergence test is implemented (RDM) in addition to the usual stopping criterion : stopping rule is when the gradients are small enough in the parameters metric (GH-1G).

Version: 2.0.1
Depends: R (≥ 2.0.0)
Imports: doParallel, foreach
Suggests: microbenchmark
Published: 2019-09-20
Author: Melanie Prague, Viviane Philipps, Cecile Proust-Lima, Boris Hejblum, Daniel Commenges, Amadou Diakite
Maintainer: Viviane Philipps <viviane.philipps at u-bordeaux.fr>
BugReports: http://github.com/VivianePhilipps/marqLevAlgParallel/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation: yes
CRAN checks: marqLevAlg results

Downloads:

Reference manual: marqLevAlg.pdf
Package source: marqLevAlg_2.0.1.tar.gz
Windows binaries: r-devel: marqLevAlg_2.0.1.zip, r-release: marqLevAlg_2.0.1.zip, r-oldrel: marqLevAlg_2.0.1.zip
OS X binaries: r-release: marqLevAlg_2.0.1.tgz, r-oldrel: marqLevAlg_2.0.1.tgz
Old sources: marqLevAlg archive

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