Implementation of the CCDr (Concave penalized Coordinate Descent with reparametrization) structure learning algorithm as described in Aragam and Zhou (2015) < http://www.jmlr.org/papers/v16/aragam15a.html>. This is a fast, score-based method for learning Bayesian networks that uses sparse regularization and block-cyclic coordinate descent.
ccdrAlgorithm implements the CCDr structure learning algorithm described in . Based on observational data, this algorithm estimates the structure of a Bayesian network (aka edges in a DAG) using penalized maximum likelihood based on L1 or concave (MCP) regularization.
Presently, this package consists of a single method that implements the main algorithm; more functionality will be provided in the future. To generate data from a given Bayesian network and/or simulate random networks, the following R packages are recommended:
bnlearn: bnlearn on CRAN, www.bnlearn.com
pcalg: pcalg on CRAN
igraph: igraph on CRAN, http://igraph.org/r/
The main method is
ccdr.run, which runs the CCDr structure learning algorithm as described in .
You can install:
the latest CRAN version with
the latest development version from GitHub with
 Aragam, B. and Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks. The Journal of Machine Learning Research. 16(Nov):2273−2328.
 Fu, F. and Zhou, Q. (2013). Learning sparse causal Gaussian networks with experimental intervention: Regularization and coordinate descent. Journal of the American Statistical Association, 108: 288-300.
ccdr.run()is now compatible with interventional data