Package: BiDAG
Type: Package
Title: Bayesian Inference for Directed Acyclic Graphs (BiDAG): Software
for the Efficient Inference and Sampling of Bayesian Networks
Version: 1.0.0
Date: 2017-07-17
Author: Polina Minkina [aut, cre], Jack Kuipers [aut]
Maintainer: Polina Minkina <polina.minkina@bsse.ethz.ch>
Description: 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.
Acknowledgments: We would like to thank Giusi Moffa for discussion and
comments on the package and its manual.
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.7), pcalg, methods, stats, utils
LinkingTo: Rcpp
RoxygenNote: 6.0.1
LazyData: TRUE
NeedsCompilation: yes
Packaged: 2017-07-19 20:02:39 UTC; me664
Repository: CRAN
Date/Publication: 2017-07-19 21:44:55 UTC

Package: BiDAG
Type: Package
Title: Bayesian Inference for Directed Acyclic Graphs (BiDAG): Software
for the Efficient Inference and Sampling of Bayesian Networks
Version: 1.0.1
Date: 2017-07-17
Author: Polina Minkina [aut, cre], Jack Kuipers [aut]
Maintainer: Polina Minkina <polina.minkina@bsse.ethz.ch>
Description: 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.
Acknowledgments: We would like to thank Giusi Moffa for discussion and
comments on the package and its manual.
License: GPL (>= 2)
Imports: Rcpp (>= 0.12.7), pcalg, methods, stats, utils
LinkingTo: Rcpp
RoxygenNote: 6.0.1
LazyData: TRUE
NeedsCompilation: yes
Packaged: 2017-07-26 14:19:47 UTC; me664
Repository: CRAN
Date/Publication: 2017-07-26 15:35:25 UTC