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

This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). 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. See Philipps et al, 2020 <arXiv:2009.03840>.

Version: 2.0.5
Depends: R (≥ 3.5.0)
Imports: doParallel, foreach
Suggests: microbenchmark, knitr, rmarkdown, rticles, ggplot2, viridis, patchwork, xtable
Published: 2021-04-01
Author: Viviane Philipps, Cecile Proust-Lima, Melanie Prague, Boris Hejblum, Daniel Commenges, Amadou Diakite
Maintainer: Viviane Philipps <viviane.philipps at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation: yes
CRAN checks: marqLevAlg results


Reference manual: marqLevAlg.pdf
Vignettes: MLA


Package source: marqLevAlg_2.0.5.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): marqLevAlg_2.0.5.tgz, r-release (x86_64): marqLevAlg_2.0.5.tgz, r-oldrel: marqLevAlg_2.0.5.tgz
Old sources: marqLevAlg archive

Reverse dependencies:

Reverse imports: sfaR


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