Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
Version: | 0.8.0.0 |
Imports: | tensorflow (≥ 2.0.0), reticulate, keras, magrittr |
Suggests: | testthat (≥ 2.1.0), knitr |
Published: | 2019-10-30 |
Author: | Sigrid Keydana [aut, cre],
Daniel Falbel [ctb],
Kevin Kuo |
Maintainer: | Sigrid Keydana <sigrid at rstudio.com> |
BugReports: | https://github.com/rstudio/tfprobability/issues |
License: | Apache License (≥ 2.0) |
URL: | https://github.com/rstudio/tfprobability |
NeedsCompilation: | no |
SystemRequirements: | TensorFlow Probability (https://www.tensorflow.org/probability) |
Materials: | README |
CRAN checks: | tfprobability results |
Reference manual: | tfprobability.pdf |
Vignettes: |
Dynamic linear models Multi-level modeling with Hamiltonian Monte Carlo Uncertainty estimates with layer_dense_variational |
Package source: | tfprobability_0.8.0.0.tar.gz |
Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
OS X binaries: | r-release: not available, r-oldrel: not available |
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