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Bioinformatics. 2017 Apr 15;33(8):1250-1252. doi: 10.1093/bioinformatics/btw807.

bnstruct: an R package for Bayesian Network structure learning in the presence of missing data.

Author information

1
IRIDIA-CoDE, Université Libre de Bruxelles, 1050 Brussels, Belgium.
2
Department of Information Engineering, University of Padova, 35131 Padova, Italy.

Abstract

Motivation:

A Bayesian Network is a probabilistic graphical model that encodes probabilistic dependencies between a set of random variables. We introduce bnstruct, an open source R package to (i) learn the structure and the parameters of a Bayesian Network from data in the presence of missing values and (ii) perform reasoning and inference on the learned Bayesian Networks. To the best of our knowledge, there is no other open source software that provides methods for all of these tasks, particularly the manipulation of missing data, which is a common situation in practice.

Availability and Implementation:

The software is implemented in R and C and is available on CRAN under a GPL licence.

Contact:

francesco.sambo@unipd.it.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28003263
DOI:
10.1093/bioinformatics/btw807
[Indexed for MEDLINE]

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