Package: BiDAG 2.1.4
BiDAG: Bayesian Inference for Directed Acyclic Graphs
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>.
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BiDAG_2.1.4.tar.gz
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BiDAG.pdf |BiDAG.html✨
BiDAG/json (API)
# Install 'BiDAG' in R: |
install.packages('BiDAG', repos = c('https://polinasuter.r-universe.dev', 'https://cloud.r-project.org')) |
- Asia - Asia dataset
- Asiamat - Asiamat
- Boston - Boston housing data
- DBNdata - Simulated data set from a 2-step dynamic Bayesian network
- DBNmat - An adjacency matrix of a dynamic Bayesian network
- DBNunrolled - An unrolled adjacency matrix of a dynamic Bayesian network
- gsim - A simulated data set from a Gaussian continuous Bayesian network
- gsim100 - A simulated data set from a Gaussian continuous Bayesian network
- gsimmat - An adjacency matrix of a simulated dataset
- interactions - Interactions dataset
- kirc - Kirc dataset
- kirp - Kirp dataset
- mapping - Mapping dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:6097d8ea15. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 06 2024 |
R-4.5-win-x86_64 | NOTE | Nov 06 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 06 2024 |
R-4.4-win-x86_64 | NOTE | Nov 06 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 06 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 06 2024 |
R-4.3-win-x86_64 | NOTE | Nov 06 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 06 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 06 2024 |
Exports:bidag2codabidag2codalistcompact2fullcompareDAGscompareDBNsconnectedSubGraphDAGscoreDBNscoreedgepfull2compactgetDAGgetMCMCscoregetRuntimegetSpacegetSubGraphgetTracegraph2miterativeMCMCitercomplearnBNm2graphmodelporderMCMCpartitionMCMCplot2in1plotDBNplotdiffsplotdiffsDBNplotpcorplotpedgessampleBNsamplecompscoreagainstDAGscoreagainstDBNscoreparametersscorespacestring2mat
Dependencies:abindbdsmatrixBHBiocGenericsBiocManagercliclueclustercodacolorspacecorpcorcpp11DEoptimRfastICAgenericsggmgluegraphigraphlatticelifecyclelmtestmagrittrMASSMatrixpcalgpkgconfigRBGLRcppRcppArmadilloRgraphvizrlangrobustbasesfsmiscvcdvctrszoo