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 |
| Reference manual: | marqLevAlg.pdf |
| Package source: | marqLevAlg_2.0.1.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_2.0.1.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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