ADtools-package: ADtools: Automatic Differentiation

In ADtools: Automatic Differentiation Toolbox

Description

Implements the forward-mode automatic differentiation for multivariate functions using the matrix-calculus notation from Magnus and Neudecker (2019) <doi:10.1002/9781119541219>. Two key features of the package are: (i) it incorporates various optimisation strategies to improve performance; this includes applying memoisation to cut down object construction time, using sparse matrix representation to speed up derivative calculation, and creating specialised matrix operations to reduce computation time; (ii) it supports differentiating random variates with respect to their parameters, targeting Markov chain Monte Carlo (MCMC) and general simulation-based applications.

Author(s)

Maintainer: Chun Fung Kwok [e-mail] (ORCID)

Authors:

• Dan Zhu [e-mail] (ORCID)

• Liana Jacobi [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/kcf-jackson/ADtools

• Report bugs at https://github.com/kcf-jackson/ADtools/issues

ADtools documentation built on Nov. 9, 2020, 5:09 p.m.