Bayesian Structure and Causal Learning of Gaussian Directed Graphs

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Documentation for package ‘BCDAG’ version 1.0.0

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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