nabla: Exact Derivatives via Automatic Differentiation

Automatic differentiation for R functions written with supported scalar arithmetic and mathematical operations. Provides a composable derivative operator D that computes gradients, Hessians, Jacobians, and arbitrary-order derivative tensors at machine precision. D(D(f)) gives Hessians, D(D(D(f))) gives third-order tensors for skewness of maximum likelihood estimators, and so on to higher orders. Supports loops and executed branches composed of supported operations. Uses nested dual numbers without finite difference truncation error; floating-point rounding and conditioning still affect results.

Version: 0.7.2
Depends: R (≥ 4.0.0), methods
Imports: stats
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown
Published: 2026-10-28
DOI: 10.32614/CRAN.package.nabla
Author: Alexander Towell ORCID iD [aut, cre]
Maintainer: Alexander Towell <queelius at gmail.com>
BugReports: https://github.com/queelius/nabla/issues
License: MIT + file LICENSE
URL: https://github.com/queelius/nabla, https://metafunctor.com
NeedsCompilation: no
Citation: nabla citation info
Materials: README, NEWS
CRAN checks: nabla results

Documentation:

Reference manual: nabla.html , nabla.pdf
Vignettes: Higher-Order Derivatives (source, R code)
Introduction to nabla (source, R code)
Higher-Order MLE Analysis (source, R code)
Gradient and Hessian Computation with nabla (source, R code)
Optimizer Integration (source, R code)

Downloads:

Package source: nabla_0.7.2.tar.gz
Windows binaries: r-devel: nabla_0.7.2.zip, r-release: nabla_0.7.2.zip, r-oldrel: nabla_0.7.1.zip
macOS binaries: r-release (arm64): nabla_0.7.2.tgz, r-oldrel (arm64): nabla_0.7.2.tgz, r-release (x86_64): nabla_0.7.2.tgz, r-oldrel (x86_64): nabla_0.7.2.tgz
Old sources: nabla archive

Linking:

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