Package: dfdr 0.2.0

dfdr: Automatic Differentiation of Simple Functions

Implementation of automatically computing derivatives of functions (see Mailund Thomas (2017) <doi:10.1007/978-1-4842-2881-4>). Moreover, calculating gradients, Hessian and Jacobian matrices is possible.

Authors:Thomas Mailund [aut], Konrad Krämer [aut, cre]

dfdr_0.2.0.tar.gz
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dfdr.pdf |dfdr.html✨
dfdr/json (API)

# Install 'dfdr' in R:
install.packages('dfdr', repos = c('https://konrad1991.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/konrad1991/dfdr/issues

On CRAN:

Conda:

5.27 score 7 stars 2 packages 11 scripts 270 downloads 7 exports 17 dependencies

Last updated 19 days agofrom:215449dc69. Checks:5 OK, 3 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 18 2025
R-4.5-winNOTEFeb 18 2025
R-4.5-macNOTEFeb 18 2025
R-4.5-linuxNOTEFeb 18 2025
R-4.4-winOKFeb 18 2025
R-4.4-macOKFeb 18 2025
R-4.3-winOKFeb 18 2025
R-4.3-macOKFeb 18 2025

Exports:dfctsfcts_add_fctgradienthessianjacobiansimplify

Dependencies:clicodetoolscpp11crayongluelifecyclelobstrmagrittrprettyunitspryrpurrrR6Rcpprlangstringistringrvctrs