Package: numDeriv 2022.9-1

Paul Gilbert

numDeriv: Accurate Numerical Derivatives

Methods for calculating (usually) accurate numerical first and second order derivatives. Accurate calculations are done using 'Richardson''s' extrapolation or, when applicable, a complex step derivative is available. A simple difference method is also provided. Simple difference is (usually) less accurate but is much quicker than 'Richardson''s' extrapolation and provides a useful cross-check. Methods are provided for real scalar and vector valued functions.

Authors:Paul Gilbert and Ravi Varadhan

numDeriv_2022.9-1.tar.gz
numDeriv_2022.9-1.zip(r-4.7)numDeriv_2022.9-1.zip(r-4.6)numDeriv_2022.9-1.zip(r-4.5)
numDeriv_2022.9-1.tgz(r-4.6-any)numDeriv_2022.9-1.tgz(r-4.5-any)
numDeriv_2022.9-1.tar.gz(r-4.7-any)numDeriv_2022.9-1.tar.gz(r-4.6-any)
numDeriv_2022.9-1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
numDeriv/json (API)
NEWS

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

Bug tracker:https://r-forge.r-project.org/projects/optimizer

On CRAN:

Conda:

14.02 score 1 stars 3.5k packages 1.7k scripts 711k downloads 10 mentions 4 exports 0 dependencies

Last updated from:67f18d75bb. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING106
source / vignettesOK123
linux-release-x86_64WARNING98
macos-release-arm64WARNING74
macos-oldrel-arm64WARNING90
windows-develWARNING122
windows-releaseWARNING71
windows-oldrelWARNING56
wasm-releaseOK82

Exports:genDgradhessianjacobian

Dependencies:

numDeriv Guide

Rendered fromGuide.Stexusingutils::Sweaveon May 28 2026.

Last update: 2012-08-14
Started: 2011-11-17

Readme and manuals

Help Manual

Help pageTopics
Accurate Numerical DerivativesnumDeriv-package numDeriv.Intro
Accurate Numerical Derivatives00.numDeriv.Intro
Generate Bates and Watts D MatrixgenD genD.default
Numerical Gradient of a Functiongrad grad.default
Calculate Hessian Matrixhessian hessian.default
Gradient of a Vector Valued Functionjacobian jacobian.default