Learn and Apply Directed Acyclic Graphs for Causal Inference

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Documentation for package ‘cia’ version 1.0.0

• DESCRIPTION file.
• Package NEWS.

Help Pages

BNLearnScorer	BNLearnScorer
CalculateAcceptanceRates	Calculate acceptance rates
CalculateEdgeProbabilities	Calculate pairwise edge probabilities
CalculateFeatureMean	Calculate arithmetic mean for a DAG feature
CollectUniqueObjects	Collect unique objects
CreateScorer	Scorer constructor
DAGtoCPDAG	Convert DAG to CPDAG
DAGtoPartition	Convert DAG to partition
DefaultProposal	Default proposal constructor
FlattenChains	Flatten chains
GetEmptyDAG	Get an empty DAG given a set of nodes.
GetIncrementalScoringEdges	Get incremental edges
GetLowestPairwiseScoringEdges	Preprocessing for blacklisting Get the lowest pairwise scoring edges.
GetMAP	Get the maximum a posteriori state
MutilateGraph	Mutilate graph
PartitionMCMC	Transition objects. Partition MCMC
PartitiontoDAG	Sample DAG from partition
PlotConcordance	Concordance plot
PlotCumulativeMeanTrace	Plot cumulative mean trace plot.
PlotScoreTrace	Plot the score trace
PostProcessChains	Index chains for further analysis
SampleChains	Sample chains
SampleEdgeProbabilities	Sample edge probabilities
SamplePosteriorPredictiveChains	Draw from a posterior predictive distribution
ScoreDAG	Score DAG.
ScoreLabelledPartition	Score labelled partition
toBNLearn	Convert to bnlearn object.
togRain	Convert to a gRain object.
toMatrix	Convert to adjacency matrix.
UniformlySampleDAG	Uniformly sample DAG
[.cia_chain	Index a cia_chain object
[.cia_chains	Index a cia_chains object
[.cia_post_chain	Indexing with respect to iterations.
[.cia_post_chains	Index a cia_post_chains object with respect to iterations.
[[.cia_chains	Index a cia_chains object
[[.cia_post_chains	Index a cia_post_chains object.