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: <https://www.causact.com/install-tensorflow-greta-and-causact.html>.
See <https://github.com/flyaflya/causact> or <https://www.causact.com/> for more documentation.
| Version: |
0.4.2 |
| Depends: |
R (≥ 3.2.0) |
| Imports: |
DiagrammeR (≥ 1.0.7), 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), lifecycle |
| Suggests: |
knitr, covr, testthat (≥ 3.0.0), rmarkdown |
| Published: |
2022-06-14 |
| Author: |
Adam Fleischhacker [aut, cre, cph],
Daniela Dapena [ctb],
Rose Nguyen [ctb],
Jared Sharpe [ctb] |
| Maintainer: |
Adam Fleischhacker <ajf at udel.edu> |
| BugReports: |
https://github.com/flyaflya/causact/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/flyaflya/causact, https://www.causact.com/ |
| 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;
https://www.tensorflow.org/); TensorFlow Probability (= v0.7.0;
https://www.tensorflow.org/probability/) |
| Materials: |
README NEWS |
| In views: |
Bayesian |
| CRAN checks: |
causact results |