The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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 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, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.
| Version: | 2.1.4 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 0.12.7), methods, graph, Rgraphviz, RBGL, pcalg, graphics, Matrix, coda |
| LinkingTo: | Rcpp |
| Published: | 2023-05-16 |
| DOI: | 10.32614/CRAN.package.BiDAG |
| Author: | Polina Suter [aut, cre], Jack Kuipers [aut] |
| Maintainer: | Polina Suter <polina.suter at gmail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Citation: | BiDAG citation info |
| CRAN checks: | BiDAG results |
| Reference manual: | BiDAG.pdf |
| Package source: | BiDAG_2.1.4.tar.gz |
| Windows binaries: | r-devel: BiDAG_2.1.4.zip, r-release: BiDAG_2.1.4.zip, r-oldrel: BiDAG_2.1.4.zip |
| macOS binaries: | r-release (arm64): BiDAG_2.1.4.tgz, r-oldrel (arm64): BiDAG_2.1.4.tgz, r-release (x86_64): BiDAG_2.1.4.tgz, r-oldrel (x86_64): BiDAG_2.1.4.tgz |
| Old sources: | BiDAG archive |
| Reverse imports: | Bestie, clustNet |
Please use the canonical form https://CRAN.R-project.org/package=BiDAG to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.