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pcalg (version 2.6-7)

Methods for Graphical Models and Causal Inference

Description

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.

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Version

Install

install.packages('pcalg')

Monthly Downloads

1,575

Version

2.6-7

License

GPL (>= 2)

Maintainer

Markus Kalisch

Last Published

October 23rd, 2019

Functions in pcalg (2.6-7)

ParDAG-class

Class "ParDAG" of Parametric Causal Models
addBgKnowledge

Add background knowledge to a CPDAG or PDAG
Score-class

Virtual Class "Score"
beta.special.pcObj

Compute set of intervention effects in a fast way
fci

Estimate a PAG by the FCI Algorithm
beta.special

Compute set of intervention effects
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)
dag2pag

Convert a DAG with latent variables into a PAG
gds

Greedy DAG Search to Estimate Markov Equivalence Class of DAG
dag2essgraph

Convert a DAG to an Essential Graph
EssGraph-class

Class "EssGraph"
ages

Estimate an APDAG within the Markov equivalence class of a DAG using AGES
GaussL0penIntScore-class

Class "GaussL0penIntScore"
find.unsh.triple

Find all Unshielded Triples in an Undirected Graph
gmD

Graphical Model Discrete 5-Dim Example Data
fciPlus

Estimate a PAG by the FCI+ Algorithm
optAdjSet

Compute the optimal adjustment set
gies

Estimate Interventional Markov Equivalence Class of a DAG by GIES
legal.path

Check if a 3-node-path is Legal
fciAlgo-class

Class "fciAlgo" of FCI Algorithm Results
gmG

Graphical Model 8-Dimensional Gaussian Example Data
mat2targets

Conversion between an intervention matrix and a list of intervention targets
gmB

Graphical Model 5-Dim Binary Example Data
ges

Estimate the Markov equivalence class of a DAG using GES
pag2mag

Transform a PAG into a MAG in the Corresponding Markov Equivalence Class
gmI

Graphical Model 7-dim IDA Data Examples
mcor

Compute (Large) Correlation Matrix
gmInt

Graphical Model 8-Dimensional Interventional Gaussian Example Data
GaussParDAG-class

Class "GaussParDAG" of Gaussian Causal Models
dsep

Test for d-separation in a DAG
binCItest

G square Test for (Conditional) Independence of Binary Variables
checkTriple

Check Consistency of Conditional Independence for a Triple of Nodes
GaussL0penObsScore-class

Class "GaussL0penObsScore"
pcalg-internal

Internal Pcalg Functions
opt.target

Get an optimal intervention target
pcalg2dagitty

Transform the adjacency matrix from pcalg into a dagitty object
pdag2dag

Extend a Partially Directed Acyclic Graph (PDAG) to a DAG
possDe

Find possible descendants of given node(s).
wgtMatrix

Weight Matrix of a Graph, e.g., a simulated DAG
r.gauss.pardag

Generate a Gaussian Causal Model Randomly
possAn

Find possible ancestors of given node(s).
randDAG

Random DAG Generation
gAlgo-class

Class "gAlgo"
corGraph

Computing the correlation graph
dag2cpdag

Convert a DAG to a CPDAG
dsepTest

Test for d-separation in a DAG
pdsep

Estimate Final Skeleton in the FCI algorithm
disCItest

G square Test for (Conditional) Independence of Discrete Variables
trueCov

Covariance matrix of a DAG.
skeleton

Estimate (Initial) Skeleton of a DAG using the PC / PC-Stable Algorithm
pcSelect

PC-Select: Estimate subgraph around a response variable
iplotPC

Plotting a pcAlgo object using the package igraph
idaFast

Multiset of Possible Total Causal Effects for Several Target Var.s
dreach

Compute D-SEP(x,y,G)
gac

Test If Set Satisfies Generalized Adjustment Criterion (GAC)
isValidGraph

Check for a DAG, CPDAG or a maximally oriented PDAG
pc

Estimate the Equivalence Class of a DAG using the PC Algorithm
jointIda

Estimate Multiset of Possible Total Joint Effects
pc.cons.intern

Utility for conservative and majority rule in PC and FCI
plotAG

Plot partial ancestral graphs (PAG)
shd

Compute Structural Hamming Distance (SHD)
pcSelect.presel

Estimate Subgraph around a Response Variable using Preselection
showAmat

Show Adjacency Matrix of pcAlgo object
plotSG

Plot the subgraph around a Specific Node in a Graph Object
udag2apag

Last step of RFCI algorithm: Transform partially oriented graph into RFCI-PAG
randomDAG

Generate a Directed Acyclic Graph (DAG) randomly
udag2pag

Last steps of FCI algorithm: Transform Final Skeleton into FCI-PAG
rmvnorm.ivent

Simulate from a Gaussian Causal Model
rmvDAG

Generate Multivariate Data according to a DAG
visibleEdge

Check visible edge.
udag2pdag

Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG
getGraph

Get the "graph" Part or Aspect of R Object
pcAlgo-class

Class "pcAlgo" of PC Algorithm Results, incl. Skeleton
ida

Estimate Multiset of Possible Joint Total Causal Effects
getNextSet

Iteration through a list of all combinations of choose(n,k)
gmL

Latent Variable 4-Dim Graphical Model Data Example
rfci

Estimate an RFCI-PAG using the RFCI Algorithm
pcAlgo

PC-Algorithm [OLD]: Estimate Skeleton or Equivalence Class of a DAG
pdag2allDags

Enumerate All DAGs in a Markov Equivalence Class
pcorOrder

Compute Partial Correlations
possibleDe

[DEPRECATED] Find possible descendants on definite status paths.
qreach

Compute Possible-D-SEP(x,G) of a node x in a PDAG G
simy

Estimate Interventional Markov Equivalence Class of a DAG
showEdgeList

Show Edge List of pcAlgo object
LINGAM

Linear non-Gaussian Acyclic Models (LiNGAM)
condIndFisherZ

Test Conditional Independence of Gaussians via Fisher's Z
compareGraphs

Compare two graphs in terms of TPR, FPR and TDR