CCDr Algorithm for Learning Sparse Gaussian Bayesian Networks

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.


Project Status: Active The project has reached a stable, usable state and is being actively developed. Travis-CI Build Status CRAN RStudio mirror downloads

ccdrAlgorithm implements the CCDr structure learning algorithm described in [1-2]. This algorithm estimates the structure of a Bayesian network from mixed observational and experimental data using penalized maximum likelihood based on L1 or concave (MCP) regularization.

Presently, this package implements the main algorithm and provides a method to simulate data from a Gaussian Bayesian network. To simulate random networks, it is recommended to use the sparsebnUtils package. Other packages for simulating DAGs and observational data include bnlearn, pcalg, and igraph.

Overview

The main method is ccdr.run, which runs the CCDr structure learning algorithm as described in [1-2]. For simulating data from a Gaussian Bayesian network, the package provides the method generate_mvn_data. This method can simulate observational data or experimental data with interventions (or combinations of both).

Installation

You can install:

  • the latest CRAN version with

    install.packages("ccdrAlgorithm")
  • the latest development version from GitHub with

    devtools::install_github(c("itsrainingdata/sparsebnUtils/dev", "itsrainingdata/ccdrAlgorithm/dev"))

References

[1] Aragam, B. and Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks. The Journal of Machine Learning Research. 16(Nov):2273−2328.

[2] Zhang, D. (2016). Concave Penalized Estimation of Causal Gaussian Networks with Intervention. Master’s thesis, UCLA.

[3] 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.

News

ccdrAlgorithm 0.0.4

Features

  • ccdr.run now supports black lists and white lists (sparsebn #6)
  • Users can now use prior knowledge of variances via the new sigmas argument to ccdr.run (sparsebn #12)

Bug fixes

  • ivn.rand = FALSE not handled correctly (#4)

ccdrAlgorithm 0.0.3

Features

  • New generate_mvn_data() method to generate multivariate normal data from a DAG.

Notes

  • Added warning when dataset contains more than 10,000 columns: This requires building from source. The CCDr algorithm has been safely tested on datasets with up to 8,000 variables.
  • By default, ccdr.run() includes the node names in the sparsebnPath output.

ccdrAlgorithm 0.0.2

Features

  • ccdr.run() is now compatible with interventional data

ccdrAlgorithm 0.0.1

  • Initial stable release

Reference manual

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install.packages("ccdrAlgorithm")

0.0.5 by Bryon Aragam, 2 months ago


https://github.com/itsrainingdata/ccdrAlgorithm


Report a bug at https://github.com/itsrainingdata/ccdrAlgorithm/issues


Browse source code at https://github.com/cran/ccdrAlgorithm


Authors: Bryon Aragam [aut, cre], Dacheng Zhang [aut]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports sparsebnUtils, Rcpp, stats, utils

Suggests testthat, graph, igraph, Matrix

Linking to Rcpp


Depended on by sparsebn.


See at CRAN