CRAN/E | baycn

baycn

Bayesian Inference for Causal Networks

Installation

About

A Bayesian hybrid approach for inferring Directed Acyclic Graphs (DAGs) for continuous, discrete, and mixed data. The algorithm can use the graph inferred by another more efficient graph inference method as input; the input graph may contain false edges or undirected edges but can help reduce the search space to a more manageable size. A Bayesian Markov chain Monte Carlo algorithm is then used to infer the probability of direction and absence for the edges in the network. References: Martin and Fu (2019) .

Key Metrics

Version 1.2.0
R ≥ 3.5.0
Published 2020-07-31 1365 days ago
Needs compilation? no
License GPL-3
License File
CRAN checks baycn results

Downloads

Yesterday 7 0%
Last 7 days 111 -15%
Last 30 days 577 -1%
Last 90 days 1.671 +5%
Last 365 days 6.373 +20%

Maintainer

Maintainer

Evan A Martin

evanamartin@gmail.com

Authors

Evan A Martin

aut / cre

Audrey Qiuyan Fu

aut

Material

Reference manual
Package source

In Views

Bayesian

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

baycn archive

Depends

R ≥ 3.5.0

Imports

egg
ggplot2
gtools
igraph
MASS
methods

Suggests

testthat