Package: pnd 0.1.3

Andreï Victorovitch Kostyrka

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.

Authors:Andreï Victorovitch Kostyrka [aut, cre]

pnd_0.1.3.tar.gz
pnd_0.1.3.zip(r-4.7)pnd_0.1.3.zip(r-4.6)pnd_0.1.3.zip(r-4.5)
pnd_0.1.3.tgz(r-4.6-any)pnd_0.1.3.tgz(r-4.5-any)
pnd_0.1.3.tar.gz(r-4.7-any)pnd_0.1.3.tar.gz(r-4.6-any)
pnd_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
pnd/json (API)

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

Bug tracker:https://github.com/fifis/pnd/issues

On CRAN:

Conda:

finite-differencesnumerical-differentiationparallel-algorithmstep-size

5.80 score 6 stars 7 scripts 544 downloads 23 exports 2 dependencies

Last updated from:a589213045. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK138
source / vignettesOK178
linux-release-x86_64OK131
macos-release-arm64OK201
macos-oldrel-arm64OK144
windows-develOK90
windows-releaseOK85
windows-oldrelOK86
wasm-releaseOK99

Exports:alignStringscheckCorescheckDimensionsdupRowIndsfdCoefformatMatGenDgenerateGridgenerateGrid2GradgradstepHessianJacobianprintMatrunParallelsolveVandermondestep.CRstep.DVstep.Kstep.Mstep.pluginstep.SWstepx

Dependencies:rbibutilsRdpack

Compatilibility of pnd with the syntax of numDeriv
Definitions related to numerical derivatives | Quick start: reproducing the numDeriv vignette | Vectorisation pitfalls | Breakdown of numDeriv::grad | Handling vectorised inputs | Approximation method | Compatibility implies syntax support, not identical values | numDeriv zero handling has a discontinuity | Not all evaluations matter for Richardson extrapolation | Diagnostics | Higher-order accuracy diagnostics | References

Last update: 2025-12-15
Started: 2024-06-26

Step-size-selection algorithm benchmark
Data-driven step-size selection procedures | Curtis--Reid (1974) bounded ratio approach | Dumontet--Vignes (1977) plug-in approach | Stepleman--Winarsky (1979) accurate-digit count estimation | Mathur (2012) AutoDX algorithm | Robust grid search | Not letting intermediate values go to waste | Non-uniform grid spacing | First-order derivatives | References

Last update: 2025-07-28
Started: 2024-06-13

Fast and accurate parallel numerical derivatives in R
Introduction | Derivative approximation via finite differences | Derivatives from Taylor series | Derivatives on arbitrary stencils | Precision loss on computers | Numerical derivatives of scalar functions | Two-sided derivatives | One-sided derivatives | Second derivatives | Higher derivatives | Fourth-order-accurate derivatives | General derivative and accuracy orders | Cross-derivatives | Gradients, Jacobians, and Hessians | Numerical gradients | Numerical Jacobians | Numerical Hessians and cross-derivatives | Exploiting the Hessian symmetry | Derivatives from existing function values | Common issues with numerical derivatives | Stencil choice | Handling very small and very large arguments | Derivatives of noisy functions | Accuracy loss due to repeated differencing | Complex derivatives | TODO -- Uncategorised | References

Last update: 2025-07-28
Started: 2024-06-13