Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Bryon Aragam, Jiaying Gu, Qing Zhou
Title: Learning Large-Scale Bayesian Networks with the sparsebn Package
Abstract: Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands - sometimes tens or hundreds of thousands - of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.

Page views:: 377. Submitted: 2017-03-07. Published: 2019-11-07.
Paper: Learning Large-Scale Bayesian Networks with the sparsebn Package     Download PDF (Downloads: 81)
Supplements:
sparsebn_0.1.0.tar.gz: R source package Download (Downloads: 6; 1MB)
v91i11.R: R replication code Download (Downloads: 6; 36KB)
v91i11-data.zip: Replication data files Download (Downloads: 4; 8MB)

DOI: 10.18637/jss.v091.i11

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.