causact: Fast, Easy, and Visual Bayesian Inference

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 interfacing with the 'numpyro' python package.

Version: 0.5.4
Depends: R (≥ 4.1.0)
Imports: DiagrammeR (≥ 1.0.9), dplyr (≥ 1.0.8), magrittr (≥ 1.5), ggplot2 (≥ 3.4.0), rlang (≥ 1.0.2), purrr (≥ 1.0.0), tidyr (≥ 1.1.4), igraph (≥ 1.2.7), stringr (≥ 1.4.1), cowplot (≥ 1.1.0), forcats (≥ 0.5.0), rstudioapi (≥ 0.11), lifecycle (≥ 1.0.2), reticulate (≥ 1.30)
Suggests: knitr, covr, testthat (≥ 3.0.0), rmarkdown, extraDistr, mvtnorm
Published: 2024-02-25
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 numpyro are needed for Bayesian inference computations; python (>= 3.8) with header files and shared library; numpyro (= v0.12.1; https://https://num.pyro.ai/en/latest/index.html); arviz (= v0.15.1; https://https://python.arviz.org/en/stable/)
Citation: causact citation info
Materials: README NEWS
In views: Bayesian
CRAN checks: causact results

Documentation:

Reference manual: causact.pdf
Vignettes: narrative-to-insight-with-causact

Downloads:

Package source: causact_0.5.4.tar.gz
Windows binaries: r-devel: causact_0.5.3.zip, r-release: causact_0.5.3.zip, r-oldrel: causact_0.5.4.zip
macOS binaries: r-release (arm64): causact_0.5.4.tgz, r-oldrel (arm64): causact_0.5.4.tgz, r-release (x86_64): causact_0.5.4.tgz
Old sources: causact archive

Linking:

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