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:
0.6.0
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)
Published:
2025年09月12日
Author:
Adam Fleischhacker [aut, cre, cph],
Daniela Dapena [ctb],
Rose Nguyen [ctb],
Jared Sharpe [ctb]
Maintainer:
Adam Fleischhacker <ajf at udel.edu>
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/)
Documentation:
Downloads:
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