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>.

Authors:Polina Suter [aut, cre], Jack Kuipers [aut]

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BiDAG/json (API)

# Install 'BiDAG' in R:
install.packages('BiDAG', repos = c('https://polinasuter.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.29 score 4 stars 2 packages 82 scripts 531 downloads 1 mentions 37 exports 37 dependencies

Last updated 2 years agofrom:6097d8ea15. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-win-x86_64NOTENov 06 2024
R-4.5-linux-x86_64NOTENov 06 2024
R-4.4-win-x86_64NOTENov 06 2024
R-4.4-mac-x86_64NOTENov 06 2024
R-4.4-mac-aarch64NOTENov 06 2024
R-4.3-win-x86_64NOTENov 06 2024
R-4.3-mac-x86_64NOTENov 06 2024
R-4.3-mac-aarch64NOTENov 06 2024

Exports:bidag2codabidag2codalistcompact2fullcompareDAGscompareDBNsconnectedSubGraphDAGscoreDBNscoreedgepfull2compactgetDAGgetMCMCscoregetRuntimegetSpacegetSubGraphgetTracegraph2miterativeMCMCitercomplearnBNm2graphmodelporderMCMCpartitionMCMCplot2in1plotDBNplotdiffsplotdiffsDBNplotpcorplotpedgessampleBNsamplecompscoreagainstDAGscoreagainstDBNscoreparametersscorespacestring2mat

Dependencies:abindbdsmatrixBHBiocGenericsBiocManagercliclueclustercodacolorspacecorpcorcpp11DEoptimRfastICAgenericsggmgluegraphigraphlatticelifecyclelmtestmagrittrMASSMatrixpcalgpkgconfigRBGLRcppRcppArmadilloRgraphvizrlangrobustbasesfsmiscvcdvctrszoo

Readme and manuals

Help Manual

Help pageTopics
Asia datasetAsia
AsiamatAsiamat
Converting a single BiDAG chain to mcmc objectbidag2coda
Converting multiple BiDAG chains to mcmc.listbidag2codalist
Boston housing dataBoston
Deriving an adjecency matrix of a full DBNcompact2full
Comparing two graphscompareDAGs
Comparing two DBNscompareDBNs
Deriving connected subgraphconnectedSubGraph
Calculating the BGe/BDe score of a single DAGDAGscore
Simulated data set from a 2-step dynamic Bayesian networkDBNdata
An adjacency matrix of a dynamic Bayesian networkDBNmat
Calculating the BGe/BDe score of a single DBNDBNscore
An unrolled adjacency matrix of a dynamic Bayesian networkDBNunrolled
Estimating posterior probabilities of single edgesedgep
Deriving a compact adjacency matrix of a DBNfull2compact
Extracting adjacency matrix (DAG) from MCMC objectgetDAG
Extracting score from MCMC objectgetMCMCscore
Extracting runtimegetRuntime
Extracting scorespace from MCMC objectgetSpace
Deriving subgraphgetSubGraph
Extracting trace from MCMC objectgetTrace
Deriving an adjacency matrix of a graphgraph2m
A simulated data set from a Gaussian continuous Bayesian networkgsim
A simulated data set from a Gaussian continuous Bayesian networkgsim100
An adjacency matrix of a simulated datasetgsimmat
interactions datasetinteractions
Structure learning with an iterative order MCMC algorithm on an expanded search spaceiterativeMCMC plot.iterativeMCMC print.iterativeMCMC summary.iterativeMCMC
iterativeMCMC class structureiterativeMCMC class
Performance assessment of iterative MCMC scheme against a known Bayesian networkitercomp plot.itercomp print.itercomp summary.itercomp
kirc datasetkirc
kirp datasetkirp
Bayesian network structure learninglearnBN
Deriving a graph from an adjacancy matrixm2graph
mapping datasetmapping
Estimating a graph corresponding to a posterior probability thresholdmodelp
Structure learning with the order MCMC algorithmorderMCMC plot.orderMCMC print.orderMCMC summary.orderMCMC
orderMCMC class structureorderMCMC class
DAG structure sampling with partition MCMCpartitionMCMC plot.partitionMCMC print.partitionMCMC summary.partitionMCMC
partitionMCMC class structurepartitionMCMC class
Highlighting similarities between two graphsplot2in1
Plotting a DBNplotDBN
Plotting difference between two graphsplotdiffs
Plotting difference between two DBNsplotdiffsDBN
Comparing posterior probabilitites of single edgesplotpcor
Plotting posterior probabilities of single edgesplotpedges
Bayesian network structure sampling from the posterior distributionsampleBN
Performance assessment of sampling algorithms against a known Bayesian networkplot.samplecomp print.samplecomp samplecomp summary.samplecomp
Calculating the score of a sample against a DAGscoreagainstDAG
Score against DBNscoreagainstDBN
Initializing score objectprint.scoreparameters scoreparameters summary.scoreparameters
Prints 'scorespace' objectprint.scorespace scorespace summary.scorespace
scorespace class structurescorespace class
Deriving interactions matrixstring2mat