Package: pcalg 2.7-12

Markus Kalisch

pcalg: 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.

Authors:Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [ctb], Marco Eigenmann [ctb], Leonard Henckel [ctb], Joris Mooij [ctb]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pcalg/json (API)

# Install 'pcalg' in R:

install.packages('pcalg', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://r-forge.r-project.org/projects/pcalg

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

gmB - Graphical Model 5-Dim Binary Example Data
gmD - Graphical Model Discrete 5-Dim Example Data
gmG - Graphical Model 8-Dimensional Gaussian Example Data
gmG8 - Graphical Model 8-Dimensional Gaussian Example Data
gmI - Graphical Model 7-dim IDA Data Examples
gmI7 - Graphical Model 7-dim IDA Data Examples
gmInt - Graphical Model 8-Dimensional Interventional Gaussian Example Data
gmL - Latent Variable 4-Dim Graphical Model Data Example

On CRAN:

openblas cpp

8.94 score 17 packages 1.3k scripts 6.7k downloads 24 mentions 95 exports 34 dependencies

Last updated from:c78fb4b98a. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	284
linux-devel-x86_64	OK	328
source / vignettes	OK	343
linux-release-arm64	OK	305
linux-release-x86_64	OK	332
macos-release-arm64	OK	252
macos-release-x86_64	OK	528
macos-oldrel-arm64	OK	258
macos-oldrel-x86_64	OK	478
windows-devel	OK	364
windows-release	OK	294
windows-oldrel	OK	376
wasm-release	OK	156

Exports:addBgKnowledge adjustment ages allDags amat2dag backdoor beta.special beta.special.pcObj binCItest causalEffect checkTriple compareGraphs condIndFisherZ corGraph dag2cpdag dag2essgraph dag2pag disCItest dreach dsep dsepAM dsepAMTest dsepTest fci fciPlus find.unsh.triple gac gaussCItest gds ges getGraph getNextSet gies gSquareBin gSquareDis ida idaFast iplotPC isValidGraph jointIda legal.path lingam LINGAM mat2targets mcor opt.target optAdjSet pag2anc pag2conf pag2edge pag2magAM pc pc.cons.intern pcalg2dagitty pcAlgo pcAlgo.Perfect pcorOrder pcSelect pcSelect.presel pdag2allDags pdag2dag pdsep plot plotAG plotSG possAn possDe possibleDe qreach r.gauss.pardag randDAG randomDAG rfci rfci.vStruc rmvDAG rmvnorm.ivent searchAM shd show showAmat showEdgeList simy skeleton summary targets2mat triple2numb trueCov udag2apag udag2pag udag2pdag udag2pdagRelaxed udag2pdagSpecial visibleEdge wgtMatrix zStat

Dependencies:abind bdsmatrix BH BiocGenerics BiocManager cli clue cluster colorspace corpcor cpp11 DEoptimR fastICA generics ggm glue graph igraph lattice lifecycle lmtest magrittr MASS Matrix pkgconfig RBGL Rcpp RcppArmadillo rlang robustbase sfsmisc vcd vctrs zoo

Overview of the 'pcalg' Package for R

Rendered fromvignette2018.Rnwusingutils::Sweave

Last update: 2024-02-06
Started: 2018-05-14

Help page	Topics
Add background knowledge to a CPDAG or PDAG	addBgKnowledge
Compute adjustment sets for covariate adjustment.	adjustment
Estimate an APDAG within the Markov equivalence class of a DAG using AGES	ages
Types and Display of Adjacency Matrices in Package 'pcalg'	amat.cpdag amat.pag amatType coerce,fciAlgo,amat-method coerce,fciAlgo,matrix-method coerce,LINGAM,amat-method coerce,pcAlgo,amat-method coerce,pcAlgo,matrix-method show.fci.amat show.pc.amat
Find Set Satisfying the Generalized Backdoor Criterion (GBC)	backdoor
Compute set of intervention effects	beta.special
Compute set of intervention effects in a fast way	beta.special.pcObj
G square Test for (Conditional) Independence of Binary Variables	binCItest gSquareBin
Check Consistency of Conditional Independence for a Triple of Nodes	checkTriple
Compare two graphs in terms of TPR, FPR and TDR	compareGraphs
Test Conditional Independence of Gaussians via Fisher's Z	condIndFisherZ gaussCItest zStat
Computing the correlation graph	corGraph
Convert a DAG to a CPDAG	dag2cpdag
Convert a DAG to an Essential Graph	dag2essgraph
Convert a DAG with latent variables into a PAG	dag2pag
G square Test for (Conditional) Independence of Discrete Variables	disCItest gSquareDis
Compute D-SEP(x,y,G)	dreach
Test for d-separation in a DAG	dsep
Test for d-separation in a MAG	dsepAM
Test for d-separation in a MAG	dsepAMTest
Test for d-separation in a DAG	dsepTest
Class '"EssGraph"'	EssGraph-class plot,EssGraph,ANY-method
Estimate a PAG with the FCI Algorithm	fci
Class "fciAlgo" of FCI Algorithm Results	fciAlgo-class plot,fciAlgo,ANY-method print.fciAlgo show,fciAlgo-method summary,fciAlgo-method
Estimate a PAG with the FCI+ Algorithm	fciPlus
Find all Unshielded Triples in an Undirected Graph	find.unsh.triple
Test If Set Satisfies Generalized Adjustment Criterion (GAC)	gac
Class '"gAlgo"'	gAlgo-class
Class '"GaussL0penIntScore"'	GaussL0penIntScore-class global.mle,GaussL0penIntScore-method global.score,GaussL0penIntScore-method local.mle,GaussL0penIntScore-method local.score,GaussL0penIntScore-method
Class '"GaussL0penObsScore"'	GaussL0penObsScore-class global.mle,GaussL0penObsScore-method global.score,GaussL0penObsScore-method local.mle,GaussL0penObsScore-method local.score,GaussL0penObsScore-method
Class '"GaussParDAG"' of Gaussian Causal Models	GaussParDAG-class
Greedy DAG Search to Estimate Markov Equivalence Class of DAG	gds
Estimate the Markov equivalence class of a DAG using GES	ges
Get the "graph" Part or Aspect of R Object	getGraph getGraph,ANY-method getGraph,fciAlgo-method getGraph,matrix-method getGraph,pcAlgo-method getGraph-methods
Iteration through a list of all combinations of choose(n,k)	getNextSet
Estimate Interventional Markov Equivalence Class of a DAG by GIES	gies
Graphical Model 5-Dim Binary Example Data	gmB
Graphical Model Discrete 5-Dim Example Data	gmD
Graphical Model 8-Dimensional Gaussian Example Data	gmG gmG8
Graphical Model 7-dim IDA Data Examples	gmI gmI7
Graphical Model 8-Dimensional Interventional Gaussian Example Data	gmInt
Latent Variable 4-Dim Graphical Model Data Example	gmL
Estimate Multiset of Possible Joint Total Causal Effects	causalEffect ida
Multiset of Possible Total Causal Effects for Several Target Var.s	idaFast
Plotting a pcAlgo object using the package igraph	iplotPC
Check for a DAG, CPDAG or a maximally oriented PDAG	isValidGraph
Estimate Multiset of Possible Total Joint Effects	jointIda
Check if a 3-node-path is Legal	legal.path
Linear non-Gaussian Acyclic Models (LiNGAM)	LINGAM lingam
Conversion between an intervention matrix and a list of intervention targets	mat2targets targets2mat
Compute (Large) Correlation Matrix	mcor
Get an optimal intervention target	opt.target
Compute the optimal adjustment set	optAdjSet
Reads off identifiable ancestors and non-ancestors from a directed PAG	pag2anc
Reads off identifiable unconfounded node pairs from a directed PAG	pag2conf
Reads off identifiable parents and non-parents from a directed PAG	pag2edge
Transform a PAG into a MAG in the Corresponding Markov Equivalence Class	pag2magAM
Class '"ParDAG"' of Parametric Causal Models	ParDAG-class plot,ParDAG,ANY-method
Estimate the Equivalence Class of a DAG using the PC Algorithm	pc
Utility for conservative and majority rule in PC and FCI	pc.cons.intern triple2numb
Transform the adjacency matrix from 'pcalg' into a 'dagitty' object	pcalg2dagitty
PC-Algorithm [OLD]: Estimate Skeleton or Equivalence Class of a DAG	pcAlgo pcAlgo.Perfect
Class "pcAlgo" of PC Algorithm Results, incl. Skeleton	pcAlgo-class plot,pcAlgo,ANY-method print.pcAlgo show,pcAlgo-method summary,pcAlgo-method
Compute Partial Correlations	pcorOrder
PC-Select: Estimate subgraph around a response variable	pcSelect
Estimate Subgraph around a Response Variable using Preselection	pcSelect.presel
Enumerate All DAGs in a Markov Equivalence Class	pdag2allDags
Extend a Partially Directed Acyclic Graph (PDAG) to a DAG	pdag2dag
Estimate Final Skeleton in the FCI algorithm	pdsep
Plot partial ancestral graphs (PAG)	plotAG
Plot the subgraph around a Specific Node in a Graph Object	plotSG
Find possible ancestors of given node(s).	possAn
Find possible descendants of given node(s).	possDe
[DEPRECATED] Find possible descendants on definite status paths.	possibleDe
Compute Possible-D-SEP(x,G) of a node x in a PDAG G	qreach
Generate a Gaussian Causal Model Randomly	r.gauss.pardag
Random DAG Generation	randDAG
Generate a Directed Acyclic Graph (DAG) randomly	randomDAG
Estimate an RFCI-PAG using the RFCI Algorithm	rfci
Generate Multivariate Data according to a DAG	rmvDAG
Simulate from a Gaussian Causal Model	rmvnorm.ivent
Virtual Class "Score"	Score-class
Search for certain nodes in a DAG/CPDAG/MAG/PAG	searchAM
Compute Structural Hamming Distance (SHD)	shd
Show Adjacency Matrix of pcAlgo object	showAmat
Show Edge List of pcAlgo object	showEdgeList
Estimate Interventional Markov Equivalence Class of a DAG	simy
Estimate (Initial) Skeleton of a DAG using the PC / PC-Stable Algorithm	skeleton
Covariance matrix of a DAG.	trueCov
Last step of RFCI algorithm: Transform partially oriented graph into RFCI-PAG	udag2apag
Last steps of FCI algorithm: Transform Final Skeleton into FCI-PAG	udag2pag
Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG	udag2pdag udag2pdagRelaxed udag2pdagSpecial
Check visible edge.	visibleEdge
Weight Matrix of a Graph, e.g., a simulated DAG	wgtMatrix

Package: pcalg 2.7-12

pcalg: Methods for Graphical Models and Causal Inference

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