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pnd  

Parallel Numerical Derivatives, Gradients, Jacobians, and Hessians of Arbitrary Accuracy Order
View on CRAN: Click here


Download and install pnd package within the R console
Install from CRAN:
install.packages("pnd")

Install from Github:
library("remotes")
install_github("cran/pnd")

Install by package version:
library("remotes")
install_version("pnd", "0.1.1")



Attach the package and use:
library("pnd")
Maintained by
Andreï Victorovitch Kostyrka
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-02-25
Latest Update: 2025-05-20
Description:
Numerical derivatives through finite-difference approximations can be calculated using the 'pnd' package with parallel capabilities and optimal step-size selection to improve accuracy. These functions facilitate efficient computation of derivatives, gradients, Jacobians, and Hessians, allowing for more evaluations to reduce the mathematical and machine errors. Designed for compatibility with the 'numDeriv' package, which has not received updates in several years, it introduces advanced features such as computing derivatives of arbitrary order, improving the accuracy of Hessian approximations by avoiding repeated differencing, and parallelising slow functions on Windows, Mac, and Linux.
How to cite:
Andreï Victorovitch Kostyrka (2025). pnd: Parallel Numerical Derivatives, Gradients, Jacobians, and Hessians of Arbitrary Accuracy Order. R package version 0.1.1, https://cran.r-project.org/web/packages/pnd. Accessed 11 Sep. 2026.
Previous versions and publish date:
(2026-07-09 06:42), 0.0.6 (2025-02-25 19:00), 0.0.7 (2025-03-02 00:10), 0.0.8 (2025-03-06 11:40), 0.0.9 (2025-03-12 00:10), 0.0.10 (2025-04-02 21:40), 0.1.0 (2025-05-20 09:40), 0.1.1 (2025-09-04 00:00), 0.1.2 (2026-02-12 20:00)
Other packages that cited pnd R package
View pnd citation profile
Other R packages that pnd depends, imports, suggests or enhances
Complete documentation for pnd
Functions, R codes and Examples using the pnd R package
Full pnd package functions and examples
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