# pcalg v2.4-5

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## Methods for Graphical Models and Causal Inference

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC) are implemented.

## Functions in pcalg

 Name Description corGraph Computing the correlation graph checkTriple Check Consistency of Conditional Independence for a Triple of Nodes dag2cpdag Convert a DAG to a CPDAG binCItest G square Test for (Conditional) Independence of Binary Variables beta.special Compute set of intervention effects amatType Types and Display of Adjacency Matrices in Package 'pcalg' condIndFisherZ Test Conditional Independence of Gaussians via Fisher's Z beta.special.pcObj Compute set of intervention effects in a fast way compareGraphs Compare two graphs in terms of TPR, FPR and TDR backdoor Find Set Satisfying the Generalized Backdoor Criterion (GBC) dsep Test for d-separation in a DAG dsepTest Test for d-separation in a DAG dreach Compute D-SEP(x,y,G) disCItest G square Test for (Conditional) Independence of Discrete Variables dag2pag Convert a DAG with latent variables into a PAG fciPlus Estimate a PAG by the FCI+ Algorithm dag2essgraph Convert a DAG to an Essential Graph fci Estimate a PAG by the FCI Algorithm EssGraph-class Class fciAlgo-class Class "fciAlgo" of FCI Algorithm Results GaussL0penObsScore-class Class gds Greedy DAG Search to Estimate Markov Equivalence Class of DAG GaussL0penIntScore-class Class getNextSet Iteration through a list of all combinations of choose(n,k) gac Test If Set Satisfies Generalized Adjustment Criterion (GAC) getGraph Get the "graph" Part or Aspect of R Object find.unsh.triple Find all Unshielded Triples in an Undirected Graph gAlgo-class Class GaussParDAG-class Class ges Estimate the Markov equivalence class of a DAG using GES iplotPC Plotting a pcAlgo object using the package igraph gmD Graphical Model Discrete 5-Dim Example Data gmB Graphical Model 5-Dim Binary Example Data gmL Latent Variable 4-Dim Graphical Model Data Example gies Estimate Interventional Markov Equivalence Class of a DAG by GIES gmG Graphical Model 8-Dimensional Gaussian Example Data idaFast Multiset of Possible Total Causal Effects for Several Target Var.s gmI Graphical Model 7-dim IDA Data Examples gmInt Graphical Model 8-Dimensional Interventional Gaussian Example Data ida Estimate Multiset of Possible Total Causal Effects mcor Compute (Large) Correlation Matrix pcalg-internal Internal Pcalg Functions LINGAM Linear non-Gaussian Acyclic Models (LiNGAM) ParDAG-class Class pc Estimate the Equivalence Class of a DAG using the PC Algorithm mat2targets Conversion between an intervention matrix and a list of intervention pc.cons.intern Utility for conservative and majority rule in PC and FCI legal.path Check if a 3-node-path is Legal pag2mag Transform a PAG into a MAG in the Corresponding Markov Equivalence Class jointIda Estimate Multiset of Possible Total Joint Effects pcSelect PC-Select: Estimate subgraph around a response variable pdag2allDags Enumerate All DAGs in a Markov Equivalence Class pdag2dag Extend a Partially Directed Acyclic Graph (PDAG) to a DAG pdsep Estimate Final Skeleton in the FCI algorithm pcAlgo PC-Algorithm [OLD]: Estimate Skeleton or Equivalence Class of a DAG pcorOrder Compute Partial Correlations plotSG Plot the subgraph around a Specific Node in a Graph Object pcSelect.presel Estimate Subgraph around a Response Variable using Preselection pcAlgo-class Class "pcAlgo" of PC Algorithm Results, incl. Skeleton plotAG Plot partial ancestral graphs (PAG) randomDAG Generate a Directed Acyclic Graph (DAG) randomly r.gauss.pardag Generate a Gaussian Causal Model Randomly possibleDe rmvnorm.ivent Simulate from a Gaussian Causal Model rfci Estimate an RFCI-PAG using the RFCI Algorithm qreach Compute Possible-D-SEP(x,G) of a node x in a PDAG G Score-class Virtual Class "Score" rmvDAG Generate Multivariate Data according to a DAG shd Compute Structural Hamming Distance (SHD) randDAG Random DAG Generation showEdgeList Show Edge List of pcAlgo object simy Estimate Interventional Markov Equivalence Class of a DAG visibleEdge skeleton Estimate (Initial) Skeleton of a DAG using the PC / PC-Stable Algorithm udag2pdag Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG trueCov unifDAG Uniform Sampling of Directed Acyclic Graphs (DAG) udag2pag Last steps of FCI algorithm: Transform Final Skeleton into FCI-PAG showAmat Show Adjacency Matrix of pcAlgo object udag2apag Last step of RFCI algorithm: Transform partially oriented graph into RFCI-PAG wgtMatrix Weight Matrix of a Graph, e.g., a simulated DAG No Results!