pcalg v2.7-1

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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), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

Functions in pcalg

Name Description
Score-class Virtual Class "Score"
adjustment Compute adjustment sets for covariate adjustment.
addBgKnowledge Add background knowledge to a CPDAG or PDAG
EssGraph-class Class "EssGraph"
ages Estimate an APDAG within the Markov equivalence class of a DAG using AGES
GaussL0penObsScore-class Class "GaussL0penObsScore"
GaussL0penIntScore-class Class "GaussL0penIntScore"
ParDAG-class Class "ParDAG" of Parametric Causal Models
GaussParDAG-class Class "GaussParDAG" of Gaussian Causal Models
LINGAM Linear non-Gaussian Acyclic Models (LiNGAM)
compareGraphs Compare two graphs in terms of TPR, FPR and TDR
dag2cpdag Convert a DAG to a CPDAG
corGraph Computing the correlation graph
amatType Types and Display of Adjacency Matrices in Package 'pcalg'
backdoor Find Set Satisfying the Generalized Backdoor Criterion (GBC)
checkTriple Check Consistency of Conditional Independence for a Triple of Nodes
binCItest G square Test for (Conditional) Independence of Binary Variables
beta.special Compute set of intervention effects
beta.special.pcObj Compute set of intervention effects in a fast way
dsepAM Test for d-separation in a MAG
dsep Test for d-separation in a DAG
condIndFisherZ Test Conditional Independence of Gaussians via Fisher's Z
dag2essgraph Convert a DAG to an Essential Graph
dag2pag Convert a DAG with latent variables into a PAG
fciAlgo-class Class "fciAlgo" of FCI Algorithm Results
fci Estimate a PAG with the FCI Algorithm
dsepAMTest Test for d-separation in a MAG
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
fciPlus Estimate a PAG with the FCI+ Algorithm
find.unsh.triple Find all Unshielded Triples in an Undirected Graph
gmB Graphical Model 5-Dim Binary Example Data
gies Estimate Interventional Markov Equivalence Class of a DAG by GIES
getGraph Get the "graph" Part or Aspect of R Object
ges Estimate the Markov equivalence class of a DAG using GES
gds Greedy DAG Search to Estimate Markov Equivalence Class of DAG
getNextSet Iteration through a list of all combinations of choose(n,k)
gac Test If Set Satisfies Generalized Adjustment Criterion (GAC)
gAlgo-class Class "gAlgo"
gmL Latent Variable 4-Dim Graphical Model Data Example
ida Estimate Multiset of Possible Joint Total Causal Effects
gmInt Graphical Model 8-Dimensional Interventional Gaussian Example Data
gmI Graphical Model 7-dim IDA Data Examples
idaFast Multiset of Possible Total Causal Effects for Several Target Var.s
iplotPC Plotting a pcAlgo object using the package igraph
gmG Graphical Model 8-Dimensional Gaussian Example Data
gmD Graphical Model Discrete 5-Dim Example Data
isValidGraph Check for a DAG, CPDAG or a maximally oriented PDAG
jointIda Estimate Multiset of Possible Total Joint Effects
pc Estimate the Equivalence Class of a DAG using the PC Algorithm
pag2mag Transform a PAG into a MAG in the Corresponding Markov Equivalence Class
mcor Compute (Large) Correlation Matrix
opt.target Get an optimal intervention target
mat2targets Conversion between an intervention matrix and a list of intervention targets
legal.path Check if a 3-node-path is Legal
optAdjSet Compute the optimal adjustment set
pag2anc Reads off identifiable ancestors and non-ancestors from a directed PAG
pag2conf Reads off identifiable unconfounded node pairs from a directed PAG
pag2edge Reads off identifiable parents and non-parents from a directed PAG
pc.cons.intern Utility for conservative and majority rule in PC and FCI
pcAlgo-class Class "pcAlgo" of PC Algorithm Results, incl. Skeleton
pcAlgo PC-Algorithm [OLD]: Estimate Skeleton or Equivalence Class of a DAG
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
pcalg-internal Internal Pcalg Functions
pcSelect.presel Estimate Subgraph around a Response Variable using Preselection
pdsep Estimate Final Skeleton in the FCI algorithm
plotAG Plot partial ancestral graphs (PAG)
pcorOrder Compute Partial Correlations
pcalg2dagitty Transform the adjacency matrix from pcalg into a dagitty object
possAn Find possible ancestors of given node(s).
plotSG Plot the subgraph around a Specific Node in a Graph Object
searchAM Search for certain nodes in a DAG/CPDAG/MAG/PAG
rmvnorm.ivent Simulate from a Gaussian Causal Model
simy Estimate Interventional Markov Equivalence Class of a DAG
showEdgeList Show Edge List of pcAlgo object
rmvDAG Generate Multivariate Data according to a DAG
rfci Estimate an RFCI-PAG using the RFCI Algorithm
udag2pdag Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG
qreach Compute Possible-D-SEP(x,G) of a node x in a PDAG G
shd Compute Structural Hamming Distance (SHD)
udag2apag Last step of RFCI algorithm: Transform partially oriented graph into RFCI-PAG
r.gauss.pardag Generate a Gaussian Causal Model Randomly
showAmat Show Adjacency Matrix of pcAlgo object
possDe Find possible descendants of given node(s).
possibleDe [DEPRECATED] Find possible descendants on definite status paths.
randomDAG Generate a Directed Acyclic Graph (DAG) randomly
udag2pag Last steps of FCI algorithm: Transform Final Skeleton into FCI-PAG
randDAG Random DAG Generation
visibleEdge Check visible edge.
skeleton Estimate (Initial) Skeleton of a DAG using the PC / PC-Stable Algorithm
wgtMatrix Weight Matrix of a Graph, e.g., a simulated DAG
trueCov Covariance matrix of a DAG.
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Vignettes of pcalg

Name
auto/Mybib.el
auto/vignette2018.el
Figure1FAT.pdf
Figure2FAT.pdf
Mybib.bib
Mybibliography.bib
jsslogo.jpg
pcalgDoc-concordance.tex
pcalgDoc.Rnw
vignette2018-concordance.tex
vignette2018.Rnw
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Details

Date 2021-1-8
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/
RoxygenNote 6.1.1
Packaged 2021-01-08 14:40:26 UTC; kalischm
Repository CRAN
Date/Publication 2021-01-09 06:30:08 UTC

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