marqLevAlg: A Parallelized General-Purpose Optimization Based on Marquardt-Levenberg Algorithm

This algorithm provides a numerical solution to the problem of minimizing (or maximizing) a function. It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives.

Version: 2.0.2
Depends: R (≥ 3.5.0)
Imports: doParallel, foreach
Suggests: microbenchmark
Published: 2020-03-30
Author: Viviane Philipps, Cecile Proust-Lima, Melanie Prague, 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_2.0.2.tar.gz
Windows binaries: r-devel: marqLevAlg_2.0.1.zip, r-devel-gcc8: marqLevAlg_2.0.1.zip, r-release: marqLevAlg_2.0.1.zip, r-oldrel: marqLevAlg_2.0.2.zip
OS X binaries: r-release: marqLevAlg_2.0.2.tgz, r-oldrel: marqLevAlg_2.0.1.tgz
Old sources: marqLevAlg archive

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