Literature DB >> 28003263

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

Alberto Franzin1,2, Francesco Sambo2, Barbara Di Camillo2.   

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.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Mesh:

Year:  2017        PMID: 28003263     DOI: 10.1093/bioinformatics/btw807

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


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