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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: | 0.5.5 |
| 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: | 2024-04-24 |
| DOI: | 10.32614/CRAN.package.causact |
| 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 |
| Reference manual: | causact.pdf |
| Vignettes: |
narrative-to-insight-with-causact |
| Package source: | causact_0.5.5.tar.gz |
| Windows binaries: | r-devel: causact_0.5.5.zip, r-release: causact_0.5.5.zip, r-oldrel: causact_0.5.5.zip |
| macOS binaries: | r-release (arm64): causact_0.5.5.tgz, r-oldrel (arm64): causact_0.5.5.tgz, r-release (x86_64): causact_0.5.5.tgz, r-oldrel (x86_64): causact_0.5.5.tgz |
| Old sources: | causact archive |
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