Bayesian Structure and Causal Learning of Gaussian Directed Graphs

Documentation for package ‘BCDAG’ version 1.0.0

acceptreject_DAG	Accept/reject the proposed DAG given the current DAG (internal function)
bd_decode	Convert strings into matrices (internal function)
bd_encode	Convert matrix into strings (internal function)
causaleffect	Compute causal effects between variables
DW_nodelml	Compute node-marginal likelihoods of a DAG model (internal function)
fa	Find the family of a node in a DAG (internal function)
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_opcard	Find the direct successors DAGs of an input DAG (internal function)
learn_DAG	MCMC scheme for Gaussian DAG posterior inference
leukemia	Protein levels for 68 diagnosed AML patients of subtype M2
new_bcdag	Create new bcdag objects
operation	Perform local moves given a DAG (internal function)
pa	Find the parents of a node in a DAG (internal function)
propose_DAG	MCMC proposal distribution (internal function)
rDAG	Generate a Directed Acyclic Graph (DAG) randomly
rDAGWishart	Random samples from a compatible DAG-Wishart distribution
rnodeDAGWishart	Draw one observation from a Normal-Inverse-Gamma distribution (internal function)
summary.bcdag	bcdag object summaries