BiDAG: Bayesian Inference for Directed Acyclic Graphs (BiDAG)
Version 1.0.2

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. References: N. Friedman and D. Koller (2003) , D. Geiger and D. Heckerman (2002) , J. Kuipers and G. Moffa (2016) , M. Kalisch et al.(2012) .

Getting started

Package details

AuthorPolina Suter [aut, cre], Jack Kuipers [aut]
Date of publication2017-09-08 14:07:33 UTC
MaintainerPolina Suter <[email protected]>
LicenseGPL (>= 2)
Version1.0.2
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("BiDAG")

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BiDAG documentation built on Sept. 8, 2017, 5:04 p.m.