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 constraintbased learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PCalgorithm, 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 or categorical 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. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter and G. Moffa (2018) <arXiv:1803.07859v2>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al.(2012) <doi:10.18637/jss.v047.i11>.
Package details 


Author  Polina Suter [aut, cre], Jack Kuipers [aut] 
Maintainer  Polina Suter <polina.minkina@bsse.ethz.ch> 
License  GPL (>= 2) 
Version  1.3.4 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.