R: Learn and Apply Directed Acyclic Graphs for Causal Inference

# Learn and Apply Directed Acyclic Graphs for Causal Inference

## Documentation for package ‘cia’ version 1.0.0

- DESCRIPTION file
- Package NEWS
- Package README

## 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.