bnstruct：一个在存在缺失数据的情况下进行贝叶斯网络结构学习的 R 包。
bnstruct: an R package for Bayesian Network structure learning in the presence of missing data.
机构信息
IRIDIA-CoDE, Université Libre de Bruxelles, 1050 Brussels, Belgium.
Department of Information Engineering, University of Padova, 35131 Padova, Italy.
出版信息
Bioinformatics. 2017 Apr 15;33(8):1250-1252. doi: 10.1093/bioinformatics/btw807.
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
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
贝叶斯网络是一种概率图形模型，它编码了一组随机变量之间的概率依赖关系。我们引入了 bnstruct，这是一个开源的 R 包，可以（i）在存在缺失值的情况下从数据中学习贝叶斯网络的结构和参数，以及（ii）对学习到的贝叶斯网络进行推理和推断。据我们所知，没有其他开源软件提供所有这些任务的方法，特别是对缺失数据的处理，这在实践中是很常见的情况。
可用性和实现
该软件是用 R 和 C 实现的，并在 CRAN 上以 GPL 许可证提供。
联系方式
补充信息
补充数据可在 Bioinformatics 在线获取。
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