dual: Automatic Differentiation with Dual Numbers
Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. The output value of a mathematical function is returned with the values of its exact first derivative (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind (2018) <https://jmlr.org/papers/volume18/17-468/17-468.pdf>.
| Version: | 0.0.5 |
| Depends: | R (≥ 3.2.0), base, stats, methods |
| Published: | 2023-10-03 |
| DOI: | 10.32614/CRAN.package.dual |
| Author: | Luca Sartore 
[aut, cre] |
| Maintainer: | Luca Sartore <drwolf85 at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| Materials: | README ChangeLog |
| In views: | NumericalMathematics |
| CRAN checks: | dual results |
Documentation:
| Reference manual: | dual.pdf |
Downloads:
| Package source: | dual_0.0.5.tar.gz |
| Windows binaries: | r-devel: dual_0.0.5.zip, r-release: dual_0.0.5.zip, r-oldrel: dual_0.0.5.zip |
| macOS binaries: | r-release (arm64): dual_0.0.5.tgz, r-oldrel (arm64): dual_0.0.5.tgz, r-release (x86_64): dual_0.0.5.tgz, r-oldrel (x86_64): dual_0.0.5.tgz |
| Old sources: | dual archive |
Linking:
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