Bayesian Inference for Directed Acyclic Graphs

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Documentation for package ‘BiDAG’ version 1.3.4

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Asia Asia dataset
Asiamat Asiamat
Boston Boston housing data
compact2full Deriving an adjecency matrix of a full DBN
compareDAGs Comparing two DAGs
compareDBNs Comparing two DBNs
dag.threshold Estimating a graph corresponding to a posterior probability threshold
DAGscore Calculating the BGe/BDe score of a single DAG
DBNdata A simulated data set from a 2-step dynamic Bayesian network A synthetic dataset containing 100 observations generated from a random dynamic Bayesian network with 12 continuous dynamic nodes and 3 static discrete nodes. The DBN imcludes observations from 5 time slices.
DBNmat An adjacency matrix of a dynamic Bayesian network
DBNscore Calculating the BGe/BDe score of a single DBN
DBNunrolled An unrolled adjacency matrix of a dynamic Bayesian network
edges.posterior Estimating posterior probabilities of single edges
full2compact Deriving a compact adjacency matrix of a DBN
graph2m Deriving an adjacency matrix of a graph
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
iterations.check Performance assessment of iterative MCMC scheme against a known Bayesian network
iterativeMCMC Structure learning with an iterative order MCMC algorithm on an expanded search space
m2graph Deriving a graph from an adjacancy matrix
orderMCMC Structure learning with the order MCMC algorithm
partitionMCMC DAG structure sampling with partition MCMC
plotDBN Plotting a DBN
plotpcor Comparing posterior probabilitites of single edges based on two samples
plotpedges Plotting posterior probabilities of single edges
sample.check Performance assessment of sampling algorithms against a known Bayesian network
scoreagainstDAG Calculating the score of a sample against a DAG
scoreparameters Initialising score object