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baycn  

Bayesian Inference for Causal Networks
View on CRAN: Click here


Download and install baycn package within the R console
Install from CRAN:
install.packages("baycn")

Install from Github:
library("remotes")
install_github("cran/baycn")

Install by package version:
library("remotes")
install_version("baycn", "1.2.0")



Attach the package and use:
library("baycn")
Maintained by
Evan A Martin
[Scholar Profile | Author Map]
First Published: 2019-10-01
Latest Update: 2020-07-31
Description:
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) .
How to cite:
Evan A Martin (2019). baycn: Bayesian Inference for Causal Networks. R package version 1.2.0, https://cran.r-project.org/web/packages/baycn. Accessed 04 May. 2025.
Previous versions and publish date:
1.0.0 (2019-10-01 17:30), 1.1.0 (2020-03-10 20:50)
Other packages that cited baycn R package
View baycn citation profile
Other R packages that baycn depends, imports, suggests or enhances
Complete documentation for baycn
Functions, R codes and Examples using the baycn R package
Some associated functions: baycn-class . drosophila . geuvadis . mhEdge . mse . plot-baycn-method . prerec . show-baycn-method . simdata . summary-baycn-method . tracePlot . 
Some associated R codes: cPrior.R . caRatio.R . carPrior.R . classes.R . convertAM.R . coordinates.R . creation.R . cycles.R . data.R . detType.R . directedAM.R . divergent.R . likelihood.R . methods.R . mhEdge.R . mse.R . mutate.R . prerec.R . simdata.R . toAdjMatrix.R . tracePlot.R . whichIt.R .  Full baycn package functions and examples
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