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, 2021 doi:10.32614/RJ-2021-089.
- Version2.0.8
- R version≥ 3.5.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Last release03/22/2023
Documentation
Team
Viviane Philipps
MaintainerShow author detailsMelanie Prague
Boris Hejblum
Cecile Proust-Lima
Amadou Diakite
Daniel Commenges
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Last 30 days
This package has been downloaded 2,567 times in the last 30 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 57 times.
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Last 365 days
This package has been downloaded 25,584 times in the last 365 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The day with the most downloads was May 15, 2024 with 135 downloads.
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Dependencies
- Imports2 packages
- Suggests7 packages
- Reverse Imports5 packages
- Reverse Suggests1 package