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baycn
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", "2.0.0") Attach the package and use:
library("baycn")
Maintained by
Evan A Martin
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[Scholar Profile | Author Map]
All associated links for this package
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 2.0.0, https://cran.r-project.org/web/packages/baycn. Accessed 05 Aug. 2026.
Previous versions and publish date:
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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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