| 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 |
| plotdiffs | Plotting difference between two graphs |
| plotdiffs.DBN | Plotting difference between two DBNs |
| 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 |