Bayesian Structure and Causal Learning of Gaussian Directed Graphs

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Documentation for package ‘BCDAG’ version 1.1.1

• 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