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: 0.5.7 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: 2025-01-15 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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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