| Literature DB >> 28003263 |
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.Mesh:
Year: 2017 PMID: 28003263 DOI: 10.1093/bioinformatics/btw807
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937