title: bnstruct: an R package for Bayesian Network structure learning in the presence of missing data.
abstract: 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.
  
  
  
  The software is implemented in R and C and is available on CRAN under a GPL licence.
  
  
  
  francesco.sambo@unipd.it.
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: IRIDIA-CoDE, Université Libre de Bruxelles, 1050 Brussels, Belgium.|Department of Information Engineering, University of Padova, 35131 Padova, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Franzin
authors.affiliation: Department of Information Engineering, University of Padova, 35131 Padova, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Sambo
authors.affiliation: Department of Information Engineering, University of Padova, 35131 Padova, Italy.
authors.forename: Barbara
authors.initials: B
authors.lastname: Di Camillo
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D015233:Models, Statistical; D012984:Software