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:
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].
The main methods for learning graphical models are:
estimate.dag
for directed acyclic graphs (Bayesian networks).estimate.precision
for undirected graphs (Markov random fields).estimate.covariance
for 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
.
You can install:
the latest CRAN version with
r
install.packages("sparsebn")
the latest development version from GitHub with
r
devtools::install_github(c("itsrainingdata/sparsebn/", "itsrainingdata/sparsebnUtils/dev", "itsrainingdata/ccdrAlgorithm/dev", "gujyjean/discretecdAlgorithm"))
[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.
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