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dbnR: Gaussian Dynamic Bayesian Network Learning and Inference in R
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DOI：10.18637/jss.v115.i06.png)
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Abstract
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Dynamic Bayesian networks are a type of multivariate time series forecasting model capable of a level of interpretability thanks to their graphical representation. They have been reported extensively in the literature in a variety of areas, but their application usually involved an ad hoc implementation or adaptation of existing Bayesian network software to a dynamic case. In this paper, we present dbnR, an R package that encapsulates the whole process of learning the model and parameters from data and performing inference. The package provides three different structure learning algorithms, exact approximate inference and a visualization tool that allows inspection of the graphical structure of the networks. The aim of dbnR is to provide a tool that enables fast deployment of dynamic Bayesian network models and to make them readily available as general purpose forecasting models.
Keywords:
dynamic Bayesian networks
multivariate time series
structure learning
forecast-ing
R
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