Man pages for BCDAG
Bayesian Structure and Causal Learning of Gaussian Directed Graphs
| as_graphNEL | Transform adjacency matrix into graphNEL object |
| causaleffect | Compute causal effects between variables |
| confint.bcdagCE | Credible Intervals for bcdagCE Object |
| get_causaleffect | Estimate total causal effects from the MCMC output |
| get_diagnostics | MCMC diagnostics |
| get_edgeprobs | Compute posterior probabilities of edge inclusion from the... |
| get_MAPdag | Compute the maximum a posteriori DAG model from the MCMC... |
| get_MPMdag | Compute the median probability DAG model from the MCMC output |
| get_neighboringDAGs | Enumerate all neighbors of a DAG |
| learn_DAG | MCMC scheme for Gaussian DAG posterior inference |
| leukemia | Protein levels for 68 diagnosed AML patients of subtype M2 |
| plot.bcdag | bcdag object plot |
| plot.bcdagCE | bcdagCE object plot |
| print.bcdag | bcdag object print |
| print.bcdagCE | bcdagCE object print |
| rDAG | Generate a Directed Acyclic Graph (DAG) randomly |
| rDAGWishart | Random samples from a compatible DAG-Wishart distribution |
| summary.bcdag | bcdag object summaries |
| summary.bcdagCE | bcdagCE object summary |
