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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) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
Getting started
Browse package contents
Package details |
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| Author | Bryon Aragam [aut, cre], Jiaying Gu [aut], Dacheng Zhang [aut], Qing Zhou [aut] |
| Maintainer | Bryon Aragam <sparsebn@gmail.com> |
| License | GPL (>= 2) |
| Version | 0.1.2 |
| URL | https://github.com/itsrainingdata/sparsebn |
| Package repository | View on CRAN |
| Installation |
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