R: Bayesian Structure and Causal Learning of Gaussian Directed Graphs

# Bayesian Structure and Causal Learning of Gaussian Directed Graphs

## Documentation for package ‘BCDAG’ version 1.1.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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 MCMC output
get_MAPdag | Compute the maximum a posteriori DAG model from the MCMC output
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