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: < http://www.causact.com/install-tensorflow-greta-and-causact.html>. See < http://github.com/flyaflya/causact> or < http://causact.com> for more documentation.


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install.packages("causact")

0.3.3 by Adam Fleischhacker, 2 months ago


https://github.com/flyaflya/causact, https://causact.com


Report a bug at https://github.com/flyaflya/causact/issues


Browse source code at https://github.com/cran/causact


Authors: Adam Fleischhacker [aut, cre, cph] , Daniela Dapena [ctb] , Rose Nguyen [ctb] , Jared Sharpe [ctb]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports DiagrammeR, dplyr, magrittr, ggplot2, rlang, greta, purrr, tidyr, igraph, stringr, cowplot, coda, forcats, htmlwidgets, rstudioapi

System requirements: 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/)


See at CRAN