Coordinate-Descent Algorithm for Learning Sparse Discrete Bayesian Networks

Structure learning of Bayesian network using coordinate-descent algorithm. This algorithm is designed for discrete network assuming a multinomial data set, and we use a multi-logit model to do the regression. The algorithm is described in Gu, Fu and Zhou (2016) .

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An algorithm to learn structure of discrete Bayesian network, this package can deal with observational data, interventional data, or a misture of both.


  • is the main function to run coordinate descent algorithm. With the adaptive option, users may choose to use regular group lasso penalty, or adaptive group lasso penalty.
  • max_lambda is a function to calculate the maximum value of lambda that will penalized all edges to zero.


Reference manual

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0.0.7 by Jiaying Gu, 2 years ago

Browse source code at

Authors: Jiaying Gu [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, sparsebnUtils, igraph

Suggests testthat

Linking to Rcpp, RcppEigen

System requirements: C++11

Depended on by sparsebn.

See at CRAN