sgmcmc: Stochastic Gradient Markov Chain Monte Carlo

Provides functions that performs popular stochastic gradient Markov chain Monte Carlo (SGMCMC) methods on user specified models. The required gradients are automatically calculated using 'TensorFlow' <https://www.tensorflow.org/>, an efficient library for numerical computation. This means only the log likelihood and log prior functions need to be specified. The methods implemented include stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian Monte Carlo (SGHMC), stochastic gradient Nose-Hoover thermostat (SGNHT) and their respective control variate versions for increased efficiency.

Version: 0.1.0
Depends: R (≥ 3.0), tensorflow
Suggests: testthat, MASS, knitr, ggplot2, rmarkdown
Published: 2017-07-18
Author: Jack Baker [aut, cre, cph], Christopher Nemeth [aut, cph], Paul Fearnhead [aut, cph], Emily B. Fox [aut, cph], STOR-i [cph]
Maintainer: Jack Baker <j.baker1 at lancaster.ac.uk>
BugReports: https://github.com/STOR-i/sgmcmc/issues
License: GPL-3
URL: https://github.com/STOR-i/sgmcmc
NeedsCompilation: no
SystemRequirements: TensorFlow (https://www.tensorflow.org/)
Materials: README
CRAN checks: sgmcmc results

Downloads:

Reference manual: sgmcmc.pdf
Vignettes: Vignette Title
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Package source: sgmcmc_0.1.0.tar.gz
Windows binaries: r-devel: sgmcmc_0.1.0.zip, r-release: sgmcmc_0.1.0.zip, r-oldrel: sgmcmc_0.1.0.zip
OS X El Capitan binaries: r-release: sgmcmc_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: sgmcmc_0.1.0.tgz

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