Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
| Version: | 0.0.5 |
| Depends: | R (≥ 3.2.3), sparsebnUtils (≥ 0.0.5), ccdrAlgorithm (≥ 0.0.4), discretecdAlgorithm (≥ 0.0.5) |
| Suggests: | knitr, rmarkdown, mvtnorm, igraph, graph, testthat |
| Published: | 2017-09-12 |
| Author: | Bryon Aragam [aut, cre], Jiaying Gu [aut], Dacheng Zhang [aut], Qing Zhou [aut] |
| Maintainer: | Bryon Aragam <sparsebn at gmail.com> |
| BugReports: | https://github.com/itsrainingdata/sparsebn/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/itsrainingdata/sparsebn |
| NeedsCompilation: | no |
| Citation: | sparsebn citation info |
| Materials: | README NEWS |
| In views: | gR |
| CRAN checks: | sparsebn results |
| Reference manual: | sparsebn.pdf |
| Vignettes: |
Introduction to sparsebn |
| Package source: | sparsebn_0.0.5.tar.gz |
| Windows binaries: | r-devel: sparsebn_0.0.5.zip, r-release: sparsebn_0.0.5.zip, r-oldrel: sparsebn_0.0.5.zip |
| OS X binaries: | r-release: sparsebn_0.0.5.tgz, r-oldrel: sparsebn_0.0.5.tgz |
| Old sources: | sparsebn archive |
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