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: 0.0.4
Depends: R (≥ 3.2.0), base, stats, methods
Published: 2022-08-31
Author: Luca Sartore ORCID iD [aut, cre]
Maintainer: Luca Sartore <drwolf85 at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: README ChangeLog
In views: NumericalMathematics
CRAN checks: dual results


Reference manual: dual.pdf


Package source: dual_0.0.4.tar.gz
Windows binaries: r-devel: dual_0.0.4.zip, r-release: dual_0.0.4.zip, r-oldrel: dual_0.0.4.zip
macOS binaries: r-release (arm64): dual_0.0.4.tgz, r-oldrel (arm64): dual_0.0.4.tgz, r-release (x86_64): dual_0.0.4.tgz, r-oldrel (x86_64): dual_0.0.4.tgz
Old sources: dual archive


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