pcalg v2.5-0

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

Functions in pcalg

Name Description
LINGAM Linear non-Gaussian Acyclic Models (LiNGAM)
ParDAG-class Class "ParDAG" of Parametric Causal Models
EssGraph-class Class "EssGraph"
GaussL0penIntScore-class Class "GaussL0penIntScore"
GaussL0penObsScore-class Class "GaussL0penObsScore"
GaussParDAG-class Class "GaussParDAG" of Gaussian Causal Models
Score-class Virtual Class "Score"
addBgKnowledge Add background knowledge to a CPDAG or PDAG
adjustment Compute adjustment sets for covariate adjustment.
amatType Types and Display of Adjacency Matrices in Package 'pcalg'
backdoor Find Set Satisfying the Generalized Backdoor Criterion (GBC)
beta.special Compute set of intervention effects
dag2cpdag Convert a DAG to a CPDAG
dsepTest Test for d-separation in a DAG
fci Estimate a PAG by the FCI Algorithm
checkTriple Check Consistency of Conditional Independence for a Triple of Nodes
compareGraphs Compare two graphs in terms of TPR, FPR and TDR
fciAlgo-class Class "fciAlgo" of FCI Algorithm Results
beta.special.pcObj Compute set of intervention effects in a fast way
binCItest G square Test for (Conditional) Independence of Binary Variables
dag2pag Convert a DAG with latent variables into a PAG
dag2essgraph Convert a DAG to an Essential Graph
find.unsh.triple Find all Unshielded Triples in an Undirected Graph
gAlgo-class Class "gAlgo"
disCItest G square Test for (Conditional) Independence of Discrete Variables
gac Test If Set Satisfies Generalized Adjustment Criterion (GAC)
gds Greedy DAG Search to Estimate Markov Equivalence Class of DAG
iplotPC Plotting a pcAlgo object using the package igraph
isValidGraph Check for a DAG, CPDAG or a maximally oriented PDAG
pag2mag Transform a PAG into a MAG in the Corresponding Markov Equivalence Class
pc Estimate the Equivalence Class of a DAG using the PC Algorithm
plotSG Plot the subgraph around a Specific Node in a Graph Object
condIndFisherZ Test Conditional Independence of Gaussians via Fisher's Z
corGraph Computing the correlation graph
dreach Compute D-SEP(x,y,G)
dsep Test for d-separation in a DAG
possAn Find possible ancestors of given node(s).
randDAG Random DAG Generation
randomDAG Generate a Directed Acyclic Graph (DAG) randomly
gmB Graphical Model 5-Dim Binary Example Data
gmD Graphical Model Discrete 5-Dim Example Data
ida Estimate Multiset of Possible Total Causal Effects
gmG Graphical Model 8-Dimensional Gaussian Example Data
gmI Graphical Model 7-dim IDA Data Examples
mat2targets Conversion between an intervention matrix and a list of intervention targets
fciPlus Estimate a PAG by the FCI+ Algorithm
getNextSet Iteration through a list of all combinations of choose(n,k)
gies Estimate Interventional Markov Equivalence Class of a DAG by GIES
jointIda Estimate Multiset of Possible Total Joint Effects
idaFast Multiset of Possible Total Causal Effects for Several Target Var.s
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
rmvnorm.ivent Simulate from a Gaussian Causal Model
shd Compute Structural Hamming Distance (SHD)
trueCov Covariance matrix of a DAG.
udag2apag Last step of RFCI algorithm: Transform partially oriented graph into RFCI-PAG
legal.path Check if a 3-node-path is Legal
pc.cons.intern Utility for conservative and majority rule in PC and FCI
pcAlgo-class Class "pcAlgo" of PC Algorithm Results, incl. Skeleton
pdsep Estimate Final Skeleton in the FCI algorithm
plotAG Plot partial ancestral graphs (PAG)
rfci Estimate an RFCI-PAG using the RFCI Algorithm
rmvDAG Generate Multivariate Data according to a DAG
ges Estimate the Markov equivalence class of a DAG using GES
getGraph Get the "graph" Part or Aspect of R Object
gmInt Graphical Model 8-Dimensional Interventional Gaussian Example Data
gmL Latent Variable 4-Dim Graphical Model Data Example
pcSelect.presel Estimate Subgraph around a Response Variable using Preselection
pcalg-internal Internal Pcalg Functions
qreach Compute Possible-D-SEP(x,G) of a node x in a PDAG G
r.gauss.pardag Generate a Gaussian Causal Model Randomly
unifDAG Uniform Sampling of Directed Acyclic Graphs (DAG)
visibleEdge Check visible edge.
mcor Compute (Large) Correlation Matrix
pcalg2dagitty Transform the adjacency matrix from pcalg into a dagitty object
pcorOrder Compute Partial Correlations
possDe Find possible descendants of given node(s).
showAmat Show Adjacency Matrix of pcAlgo object
showEdgeList Show Edge List of pcAlgo object
udag2pag Last steps of FCI algorithm: Transform Final Skeleton into FCI-PAG
udag2pdag Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG
possibleDe [DEPRECATED] Find possible descendants on definite status paths.
simy Estimate Interventional Markov Equivalence Class of a DAG
skeleton Estimate (Initial) Skeleton of a DAG using the PC / PC-Stable Algorithm
wgtMatrix Weight Matrix of a Graph, e.g., a simulated DAG
No Results!

Vignettes of pcalg

Name
Figure1FAT.pdf
Figure2FAT.pdf
Mybib.bib
jsslogo.jpg
pcalgDoc.Rnw
No Results!

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Details

Date 2017-07-11
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 2017-07-11 15:04:57 UTC; kalischm
Repository CRAN
Date/Publication 2017-07-12 10:41:16 UTC

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