causact: Accelerated Bayesian Analytics with DAGs

Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on the sleek and elegant 'greta' package for Bayesian inference. 'greta', in turn, is an interface into 'TensorFlow' from 'R'. Install 'greta' using instructions available here: <>. See <> or <> for more documentation.

Version: 0.3.2
Depends: R (≥ 3.2.0)
Imports: DiagrammeR (≥ 1.0.6), dplyr (≥ 0.8.5), magrittr (≥ 1.5), ggplot2 (≥ 3.3.0), rlang (≥ 0.4.6), greta (≥ 0.3.1), purrr (≥ 0.3.4), tidyr (≥ 1.0.3), igraph (≥ 1.2.5), stringr (≥ 1.4.0), cowplot (≥ 1.0.0), coda (≥ 0.19.3), forcats (≥ 0.5.0), htmlwidgets (≥ 1.5.1), rstudioapi (≥ 0.11)
Published: 2020-07-09
Author: Adam Fleischhacker [aut, cre, cph], Daniela Dapena [ctb], Rose Nguyen [ctb], Jared Sharpe [ctb]
Maintainer: Adam Fleischhacker <ajf at>
License: MIT + file LICENSE
NeedsCompilation: no
SystemRequirements: Python and TensorFlow are needed for Bayesian inference computations; Python (>= 2.7.0) with header files and shared library; TensorFlow (= v1.14;; TensorFlow Probability (= v0.7.0; UTF-8
Materials: README NEWS
CRAN checks: causact results


Reference manual: causact.pdf
Package source: causact_0.3.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: causact_0.3.1.tgz, r-oldrel: causact_0.3.1.tgz
Old sources: causact archive


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