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 as described in Aragam, Gu, and Zhou (2017) <https://arxiv.org/abs/1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.

Install the latest version of this package by entering the following in R:
install.packages("sparsebn")
AuthorBryon Aragam [aut, cre]
Date of publication2017-03-16 01:08:03
MaintainerBryon Aragam <sparsebn@gmail.com>
LicenseGPL (>= 2)
Version0.0.4
https://github.com/itsrainingdata/sparsebn

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Files

inst
inst/CITATION
inst/doc
inst/doc/sparsebn-vignette.html
inst/doc/sparsebn-vignette.Rmd
inst/doc/sparsebn-vignette.R
tests
tests/testthat.R
tests/testthat
tests/testthat/Rplots.pdf
tests/testthat/helper-sparsebnUtils-generate_objects.R tests/testthat/test-dag.R tests/testthat/test-covariance.R tests/testthat/test-plotDAG.R tests/testthat/test-precision.R
NAMESPACE
NEWS.md
data
data/pathfinder.rda
data/cytometryContinuous.rda
data/cytometryDiscrete.rda
R
R/sparsebn.R R/sparsebn-plotting.R R/data.R R/sparsebn-main.R R/zzz.R
vignettes
vignettes/sparsebn-vignette.Rmd
README.md
MD5
build
build/vignette.rds
DESCRIPTION
man
man/sparsebn.Rd man/cytometryDiscrete.Rd man/pathfinder.Rd man/cytometryContinuous.Rd man/plotDAG.Rd man/estimate.dag.Rd man/estimate.covariance.Rd

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