A Shiny application for learning Bayesian Decision Networks from data. This package can be used for probabilistic reasoning (in the observational setting), causal inference (in the presence of interventions) and learning policy decisions (in Decision Network setting). Functionalities include end-to-end implementations for data-preprocessing, structure-learning, exact inference, approximate inference, extending the learned structure to Decision Networks and policy optimization using statistically rigorous methods such as bootstraps, resampling, ensemble-averaging and cross-validation. In addition to Bayesian Decision Networks, it also features correlation networks, community-detection, graph visualizations, graph exports and web-deployment of the learned models as Shiny dashboards.
|Author||Tavpritesh Sethi [aut, cre], Shubham Maheshwari [aut]|
|Maintainer||Tavpritesh Sethi <firstname.lastname@example.org>|
|License||GPL-3 | file LICENSE|
|Package repository||View on GitHub|
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