pnd: Parallel Numerical Derivatives, Gradients, Jacobians, and Hessians of Arbitrary Accuracy Order

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.

Package details

AuthorAndreï Victorovitch Kostyrka [aut, cre]
MaintainerAndreï Victorovitch Kostyrka <andrei.kostyrka@gmail.com>
LicenseEUPL
Version0.1.1
URL https://github.com/Fifis/pnd
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("pnd")

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pnd documentation built on Sept. 9, 2025, 5:44 p.m.