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.
|Author||Bryon Aragam [aut, cre]|
|Date of publication||2017-03-16 01:08:03|
|Maintainer||Bryon Aragam <email@example.com>|
|License||GPL (>= 2)|
cytometryContinuous: The continuous cytometry network
cytometryDiscrete: The discrete cytometry network
estimate.covariance: Covariance estimation
estimate.dag: Estimate a DAG from data
pathfinder: The pathfinder network
plotDAG: Plot a DAG
sparsebn: sparsebn: Learning Sparse Bayesian Networks from...
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.