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ADtools: Automatic Differentiation Toolbox
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.
Getting started
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Package details |
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| Author | Chun Fung Kwok [aut, cre] (<https://orcid.org/0000-0002-0716-3879>), Dan Zhu [aut] (<https://orcid.org/0000-0003-1487-2232>), Liana Jacobi [aut] (<https://orcid.org/0000-0001-7210-0500>) |
| Maintainer | Chun Fung Kwok <kwokcf@unimelb.edu.au> |
| License | MIT + file LICENSE |
| Version | 0.5.4 |
| URL | https://github.com/kcf-jackson/ADtools |
| Package repository | View on CRAN |
| Installation |
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