sparsebn
Learning Sparse Bayesian Networks from High-Dimensional Data
Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017)
sparsebn
Introducing sparsebn: A new R package for learning sparse Bayesian
networks and other graphical models from high-dimensional data via
sparse regularization. Designed from the ground up to handle:
- Experimental data with interventions
- Mixed observational / experimental data
- High-dimensional data with p >> n
- Datasets with thousands of variables (tested up to p=8000)
- Continuous and discrete data
The emphasis of this package is scalability and statistical consistency
on high-dimensional datasets. Compared to existing algorithms,
sparsebn scales much better and is under active development. For more
details on this package, including worked examples and the
methodological background, please see our new
preprint [1].
Overview
The main methods for learning graphical models are:
estimate.dagfor directed acyclic graphs (Bayesian networks).estimate.precisionfor undirected graphs (Markov random fields).estimate.covariancefor covariance matrices.
Currently, estimation of precision and covariances matrices is limited to Gaussian data.
The workhorse behind
sparsebn is the
sparsebnUtils
package, which provides various S3 classes and methods for representing
and manipulating graphs. The basic algorithms are implemented in
ccdrAlgorithm and
discretecdAlgorithm.
Installation
You can install:
-
the latest CRAN version with
install.packages("sparsebn") -
the latest development version from GitHub with
devtools::install_github(c("itsrainingdata/sparsebn/", "itsrainingdata/sparsebnUtils/dev", "itsrainingdata/ccdrAlgorithm/dev", "gujyjean/discretecdAlgorithm"))
References
[1] Aragam, B., Gu, J., and Zhou, Q. (2017). Learning large-scale Bayesian networks with the sparsebn package. arXiv: 1703.04025.
[2] Aragam, B. and Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks. The Journal of Machine Learning Research. 16(Nov):2273−2328.
[3] Fu, F., Gu, J., and Zhou, Q. (2014). Adaptive penalized estimation of directed acyclic graphs from categorical data. arXiv: 1403.2310.
[4] Aragam, B., Amini, A. A., and Zhou, Q. (2015). Learning directed acyclic graphs with penalized neighbourhood regression. arXiv: 1511.08963.
[5] 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.