Automatic Differentiation Toolbox

Implements the forward-mode automatic differentiation for multivariate functions using the matrix-calculus notation from Magnus and Neudecker (2019) . 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.


Reference manual

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0.5.4 by Chun Fung Kwok, 9 months ago

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Authors: Chun Fung Kwok [aut, cre] , Dan Zhu [aut] , Liana Jacobi [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports purrr, dplyr, magrittr, assertthat, mvtnorm, Rcpp

Depends on methods, Matrix

Suggests testthat, covr, knitr, rmarkdown, pryr, MCMCpack

Linking to Rcpp, RcppArmadillo

See at CRAN