pcalg v2.4-5

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by Markus Kalisch

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
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Details

Date 2017-02-17
LinkingTo Rcpp (>= 0.11.0), RcppArmadillo, BH
ByteCompile yes
NeedsCompilation yes
Encoding UTF-8
License GPL (>= 2)
URL http://pcalg.r-forge.r-project.org/
Packaged 2017-02-21 20:16:13 UTC; kalischm
Repository CRAN
Date/Publication 2017-02-22 09:57:28

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