tfprobability: Interface to 'TensorFlow Probability'

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.

Imports: tensorflow (≥ 2.4.0), reticulate, keras, magrittr
Suggests: tfdatasets, testthat (≥ 2.1.0), knitr, rmarkdown
Published: 2021-05-20
Author: Sigrid Keydana [aut, cre], Daniel Falbel [ctb], Kevin Kuo ORCID iD [ctb], RStudio [cph]
Maintainer: Sigrid Keydana <sigrid at>
License: Apache License (≥ 2.0)
NeedsCompilation: no
SystemRequirements: TensorFlow Probability (
Materials: README NEWS
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_0.12.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): tfprobability_0.12.0.0.tgz, r-release (x86_64): tfprobability_0.12.0.0.tgz, r-oldrel: tfprobability_0.12.0.0.tgz
Old sources: tfprobability archive

Reverse dependencies:

Reverse imports: deepregression, ML2Pvae


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