Asia | Asia dataset |

Asiamat | Asiamat |

Boston | Boston housing data |

compact2full | Deriving an adjecency matrix of a full DBN |

compareDAGs | Comparing two graphs |

compareDBNs | Comparing two DBNs |

connectedSubGraph | Deriving connected subgraph |

DAGscore | Calculating the BGe/BDe score of a single DAG |

DBNdata | 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 nodes. The DBN includes 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 |

edgep | Estimating posterior probabilities of single edges |

full2compact | Deriving a compact adjacency matrix of a DBN |

getSubGraph | Deriving subgraph |

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 |

interactions | interactions dataset |

iterativeMCMC | Structure learning with an iterative order MCMC algorithm on an expanded search space |

iterativeMCMC class | iterativeMCMC class structure |

itercomp | Performance assessment of iterative MCMC scheme against a known Bayesian network |

kirc | kirc dataset |

kirp | kirp dataset |

m2graph | Deriving a graph from an adjacancy matrix |

mapping | mapping dataset |

modelp | Estimating a graph corresponding to a posterior probability threshold |

orderMCMC | Structure learning with the order MCMC algorithm |

orderMCMC class | orderMCMC class structure |

partitionMCMC | DAG structure sampling with partition MCMC |

partitionMCMC class | partitionMCMC class structure |

plot.iterativeMCMC | Structure learning with an iterative order MCMC algorithm on an expanded search space |

plot.itercomp | Performance assessment of iterative MCMC scheme against a known Bayesian network |

plot.orderMCMC | Structure learning with the order MCMC algorithm |

plot.partitionMCMC | DAG structure sampling with partition MCMC |

plot.samplecomp | Performance assessment of sampling algorithms against a known Bayesian network |

plot2in1 | Highlighting similarities between two graphs |

plotDBN | Plotting a DBN |

plotdiffs | Plotting difference between two graphs |

plotdiffs.DBN | Plotting difference between two DBNs |

plotpcor | Comparing posterior probabilitites of single edges |

plotpedges | Plotting posterior probabilities of single edges |

print.iterativeMCMC | Structure learning with an iterative order MCMC algorithm on an expanded search space |

print.itercomp | Performance assessment of iterative MCMC scheme against a known Bayesian network |

print.orderMCMC | Structure learning with the order MCMC algorithm |

print.partitionMCMC | DAG structure sampling with partition MCMC |

print.samplecomp | Performance assessment of sampling algorithms against a known Bayesian network |

print.scoreparameters | Initializing score object |

print.scorespace | Prints 'scorespace' object |

samplecomp | Performance assessment of sampling algorithms against a known Bayesian network |

scoreagainstDAG | Calculating the score of a sample against a DAG |

scoreparameters | Initializing score object |

scorespace | Prints 'scorespace' object |

scorespace class | scorespace class structure |

string2mat | Deriving interactions matrix |

summary.iterativeMCMC | Structure learning with an iterative order MCMC algorithm on an expanded search space |

summary.itercomp | Performance assessment of iterative MCMC scheme against a known Bayesian network |

summary.orderMCMC | Structure learning with the order MCMC algorithm |

summary.partitionMCMC | DAG structure sampling with partition MCMC |

summary.samplecomp | Performance assessment of sampling algorithms against a known Bayesian network |

summary.scoreparameters | Initializing score object |

summary.scorespace | Prints 'scorespace' object |