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: |
0.1.0 |
| 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: |
2019-11-04 |
| 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 |