CRAN/E | causact

causact

Fast, Easy, and Visual Bayesian Inference

Installation

About

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.

Citation causact citation info
github.com/flyaflya/causact
www.causact.com/
System requirements 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/)
Bug report File report

Key Metrics

Version 0.5.4
R ≥ 4.1.0
Published 2024-02-25 55 days ago
Needs compilation? no
License MIT
License File
CRAN checks causact results

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Maintainer

Maintainer

Adam Fleischhacker

ajf@udel.edu

Authors

Adam Fleischhacker

aut / cre / cph

Daniela Dapena

ctb

Rose Nguyen

ctb

Jared Sharpe

ctb

Material

README
NEWS
Reference manual
Package source

In Views

Bayesian

Vignettes

narrative-to-insight-with-causact

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

causact archive

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