{ "_id": "6a22a1d5cd65a98ecbd58b39", "Package": "pcalg", "Version": "2.7-12", "Date": "2024-09-12", "Title": "Methods for Graphical Models and Causal Inference", "Description": "Functions for causal structure learning and causal\ninference using graphical models. The main algorithms for\ncausal structure learning are PC (for observational data\nwithout hidden variables), FCI and RFCI (for observational data\nwith hidden variables), and GIES (for a mix of data from\nobservational studies (i.e. observational data) and data from\nexperiments involving interventions (i.e. interventional data)\nwithout hidden variables). For causal inference the IDA\nalgorithm, the Generalized Backdoor Criterion (GBC), the\nGeneralized Adjustment Criterion (GAC) and some related\nfunctions are implemented. Functions for incorporating\nbackground knowledge are provided.", "Maintainer": "Markus Kalisch ", "Authors@R": "c(person(\"Markus\",\"Kalisch\",\nemail=\"kalisch@stat.math.ethz.ch\", role=c(\"aut\",\"cre\")),\nperson(\"Alain\", \"Hauser\", role=\"aut\"), person(\"Martin\",\"Maechler\", role=\"aut\"),\nperson(\"Diego\", \"Colombo\", role=\"ctb\"), person(\"Doris\", \"Entner\", role=\"ctb\"),\nperson(\"Patrik\", \"Hoyer\", role=\"ctb\"), person(\"Antti\", \"Hyttinen\", role=\"ctb\"),\nperson(\"Jonas\", \"Peters\", role=\"ctb\"), person(\"Nicoletta\", \"Andri\", role=\"ctb\"),\nperson(\"Emilija\", \"Perkovic\", role=\"ctb\"), person(\"Preetam\", \"Nandy\", role=\"ctb\"),\nperson(\"Philipp\", \"Ruetimann\", role=\"ctb\"),person(\"Daniel\", \"Stekhoven\", role=\"ctb\"),\nperson(\"Manuel\", \"Schuerch\", role=\"ctb\"), person(\"Marco\", \"Eigenmann\", role=\"ctb\"),\nperson(\"Leonard\", \"Henckel\", role = \"ctb\"),person(\"Joris\", \"Mooij\", role = \"ctb\"))", "NeedsCompilation": "yes", "Encoding": "UTF-8", "License": "GPL (>= 2)", "URL": "https://pcalg.r-forge.r-project.org/", "Config/pak/sysreqs": "libglpk-dev libxml2-dev", "Repository": "https://r-forge.r-universe.dev", "Date/Publication": "2024-09-12 11:09:43 UTC", "RemoteUrl": "https://github.com/r-forge/pcalg", "RemoteRef": "HEAD", "RemoteSha": "c78fb4b98a57936372eb6bb71bb1a28c5edae9e8", "RemoteSubdir": "pkg", "Packaged": { "Date": "2026-06-05 10:04:42 UTC", "User": "root" }, "Author": "Markus Kalisch [aut, cre],\nAlain Hauser [aut],\nMartin Maechler [aut],\nDiego Colombo [ctb],\nDoris Entner [ctb],\nPatrik Hoyer [ctb],\nAntti Hyttinen [ctb],\nJonas Peters [ctb],\nNicoletta Andri [ctb],\nEmilija Perkovic [ctb],\nPreetam Nandy [ctb],\nPhilipp Ruetimann [ctb],\nDaniel Stekhoven [ctb],\nManuel Schuerch [ctb],\nMarco Eigenmann [ctb],\nLeonard Henckel [ctb],\nJoris Mooij [ctb]", "MD5sum": "0eac402b9a4561cdcca98b9ede87a1a3", "_user": "r-forge", "_type": "src", "_file": "pcalg_2.7-12.tar.gz", "_fileid": 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}, { "version": "2.7-12", "date": "2024-09-12" } ], "_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" ], "_datasets": [ { "name": "gmB", "title": "Graphical Model 5-Dim Binary Example Data", "object": "gmB", "file": "gmB.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "gmD", "title": "Graphical Model Discrete 5-Dim Example Data", "object": "gmD", "file": "gmD.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "gmG", "title": "Graphical Model 8-Dimensional Gaussian Example Data", "object": "gmG", "file": "gmG.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "gmG8", "title": "Graphical Model 8-Dimensional Gaussian Example Data", "object": "gmG", "file": "gmG.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "gmI", "title": "Graphical Model 7-dim IDA Data Examples", "object": "gmI", "file": "gmI.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "gmI7", "title": "Graphical Model 7-dim IDA Data Examples", "object": "gmI", "file": "gmI.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "gmInt", "title": "Graphical Model 8-Dimensional Interventional Gaussian Example Data", "object": "gmInt", "file": "gmInt.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false }, { "name": "gmL", "title": "Latent Variable 4-Dim Graphical Model Data Example", "object": "gmL", "file": "gmL.rda", "class": [ "list" ], "fields": [], "table": false, "tojson": false } ], "_help": [ { "page": "addBgKnowledge", "title": "Add background knowledge to a CPDAG or PDAG", "topics": [ "addBgKnowledge" ] }, { "page": "adjustment", "title": "Compute adjustment sets for covariate adjustment.", "topics": [ "adjustment" ] }, { "page": "ages", "title": "Estimate an APDAG within the Markov equivalence class of a DAG using AGES", "topics": [ "ages" ] }, { "page": "amatType", "title": "Types and Display of Adjacency Matrices in Package 'pcalg'", "topics": [ "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" ] }, { "page": "backdoor", "title": "Find Set Satisfying the Generalized Backdoor Criterion (GBC)", "topics": [ "backdoor" ] }, { "page": "beta.special", "title": "Compute set of intervention effects", "topics": [ "beta.special" ] }, { "page": "beta.special.pcObj", "title": "Compute set of intervention effects in a fast way", "topics": [ "beta.special.pcObj" ] }, { "page": "binCItest", "title": "G square Test for (Conditional) Independence of Binary Variables", "topics": [ "binCItest", "gSquareBin" ] }, { "page": "checkTriple", "title": "Check Consistency of Conditional Independence for a Triple of Nodes", "topics": [ "checkTriple" ] }, { "page": "compareGraphs", "title": "Compare two graphs in terms of TPR, FPR and TDR", "topics": [ "compareGraphs" ] }, { "page": "condIndFisherZ", "title": "Test Conditional Independence of Gaussians via Fisher's Z", "topics": [ "condIndFisherZ", "gaussCItest", "zStat" ] }, { "page": "corGraph", "title": "Computing the correlation graph", "topics": [ "corGraph" ] }, { "page": "dag2cpdag", "title": "Convert a DAG to a CPDAG", "topics": [ "dag2cpdag" ] }, { "page": "dag2essgraph", "title": "Convert a DAG to an Essential Graph", "concept": [ "DAG", "CPDAG", "essential graph" ], "topics": [ "dag2essgraph" ] }, { "page": "dag2pag", "title": "Convert a DAG with latent variables into a PAG", "topics": [ "dag2pag" ] }, { "page": "disCItest", "title": "G square Test for (Conditional) Independence of Discrete Variables", "topics": [ "disCItest", "gSquareDis" ] }, { "page": "dreach", "title": "Compute D-SEP(x,y,G)", "topics": [ "dreach" ] }, { "page": "dsep", "title": "Test for d-separation in a DAG", "topics": [ "dsep" ] }, { "page": "dsepAM", "title": "Test for d-separation in a MAG", "topics": [ "dsepAM" ] }, { "page": "dsepAMTest", "title": "Test for d-separation in a MAG", "topics": [ "dsepAMTest" ] }, { "page": "dsepTest", "title": "Test for d-separation in a DAG", "topics": [ "dsepTest" ] }, { "page": "EssGraph-class", "title": "Class '\"EssGraph\"'", "topics": [ "EssGraph-class", "plot,EssGraph,ANY-method" ] }, { "page": "fci", "title": "Estimate a PAG with the FCI Algorithm", "topics": [ "fci" ] }, { "page": "fciAlgo-class", "title": "Class \"fciAlgo\" of FCI Algorithm Results", "topics": [ "fciAlgo-class", "plot,fciAlgo,ANY-method", "print.fciAlgo", "show,fciAlgo-method", "summary,fciAlgo-method" ] }, { "page": "fciPlus", "title": "Estimate a PAG with the FCI+ Algorithm", "topics": [ "fciPlus" ] }, { "page": "find.unsh.triple", "title": "Find all Unshielded Triples in an Undirected Graph", "topics": [ "find.unsh.triple" ] }, { "page": "gac", "title": "Test If Set Satisfies Generalized Adjustment Criterion (GAC)", "topics": [ "gac" ] }, { "page": "gAlgo-class", "title": "Class '\"gAlgo\"'", "topics": [ "gAlgo-class" ] }, { "page": "GaussL0penIntScore-class", "title": "Class '\"GaussL0penIntScore\"'", "topics": [ "GaussL0penIntScore-class", "global.mle,GaussL0penIntScore-method", "global.score,GaussL0penIntScore-method", "local.mle,GaussL0penIntScore-method", "local.score,GaussL0penIntScore-method" ] }, { "page": "GaussL0penObsScore-class", "title": "Class '\"GaussL0penObsScore\"'", "topics": [ "GaussL0penObsScore-class", "global.mle,GaussL0penObsScore-method", "global.score,GaussL0penObsScore-method", "local.mle,GaussL0penObsScore-method", "local.score,GaussL0penObsScore-method" ] }, { "page": "GaussParDAG-class", "title": "Class '\"GaussParDAG\"' of Gaussian Causal Models", "concept": [ "Gaussian causal model", "structural equation model" ], "topics": [ "GaussParDAG-class" ] }, { "page": "gds", "title": "Greedy DAG Search to Estimate Markov Equivalence Class of DAG", "concept": [ "greedy DAG search", "essential graph" ], "topics": [ "gds" ] }, { "page": "ges", "title": "Estimate the Markov equivalence class of a DAG using GES", "concept": [ "greedy equivalence search", "essential graph", "CPDAG" ], "topics": [ "ges" ] }, { "page": "getGraph", "title": "Get the \"graph\" Part or Aspect of R Object", "topics": [ "getGraph", "getGraph,ANY-method", "getGraph,fciAlgo-method", "getGraph,matrix-method", "getGraph,pcAlgo-method", "getGraph-methods" ] }, { "page": "getNextSet", "title": "Iteration through a list of all combinations of choose(n,k)", "topics": [ "getNextSet" ] }, { "page": "gies", "title": "Estimate Interventional Markov Equivalence Class of a DAG by GIES", "concept": [ "greedy interventional equivalence search", "essential graph" ], "topics": [ "gies" ] }, { "page": "gmB", "title": "Graphical Model 5-Dim Binary Example Data", "topics": [ "gmB" ] }, { "page": "gmD", "title": "Graphical Model Discrete 5-Dim Example Data", "topics": [ "gmD" ] }, { "page": "gmG", "title": "Graphical Model 8-Dimensional Gaussian Example Data", "topics": [ "gmG", "gmG8" ] }, { "page": "gmI", "title": "Graphical Model 7-dim IDA Data Examples", "topics": [ "gmI", "gmI7" ] }, { "page": "gmInt", "title": "Graphical Model 8-Dimensional Interventional Gaussian Example Data", "topics": [ "gmInt" ] }, { "page": "gmL", "title": "Latent Variable 4-Dim Graphical Model Data Example", "topics": [ "gmL" ] }, { "page": "ida", "title": "Estimate Multiset of Possible Joint Total Causal Effects", "topics": [ "causalEffect", "ida" ] }, { "page": "idaFast", "title": "Multiset of Possible Total Causal Effects for Several Target Var.s", "topics": [ "idaFast" ] }, { "page": "iplotPC", "title": "Plotting a pcAlgo object using the package igraph", "topics": [ "iplotPC" ] }, { "page": "isValidGraph", "title": "Check for a DAG, CPDAG or a maximally oriented PDAG", "topics": [ "isValidGraph" ] }, { "page": "jointIda", "title": "Estimate Multiset of Possible Total Joint Effects", "topics": [ "jointIda" ] }, { "page": "legal.path", "title": "Check if a 3-node-path is Legal", "topics": [ "legal.path" ] }, { "page": "LINGAM", "title": "Linear non-Gaussian Acyclic Models (LiNGAM)", "topics": [ "LINGAM", "lingam" ] }, { "page": "mat2targets", "title": "Conversion between an intervention matrix and a list of intervention targets", "concept": [ "intervention", "interventional data" ], "topics": [ "mat2targets", "targets2mat" ] }, { "page": "mcor", "title": "Compute (Large) Correlation Matrix", "topics": [ "mcor" ] }, { "page": "opt.target", "title": "Get an optimal intervention target", "concept": [ "essential graph", "CPDAG", "interventions" ], "topics": [ "opt.target" ] }, { "page": "optAdjSet", "title": "Compute the optimal adjustment set", "topics": [ "optAdjSet" ] }, { "page": "pag2anc", "title": "Reads off identifiable ancestors and non-ancestors from a directed PAG", "topics": [ "pag2anc" ] }, { "page": "pag2conf", "title": "Reads off identifiable unconfounded node pairs from a directed PAG", "topics": [ "pag2conf" ] }, { "page": "pag2edge", "title": "Reads off identifiable parents and non-parents from a directed PAG", "topics": [ "pag2edge" ] }, { "page": "pag2mag", "title": "Transform a PAG into a MAG in the Corresponding Markov Equivalence Class", "topics": [ "pag2magAM" ] }, { "page": "ParDAG-class", "title": "Class '\"ParDAG\"' of Parametric Causal Models", "topics": [ "ParDAG-class", "plot,ParDAG,ANY-method" ] }, { "page": "pc", "title": "Estimate the Equivalence Class of a DAG using the PC Algorithm", "topics": [ "pc" ] }, { "page": "pc.cons.intern", "title": "Utility for conservative and majority rule in PC and FCI", "topics": [ "pc.cons.intern", "triple2numb" ] }, { "page": "pcalg2dagitty", "title": "Transform the adjacency matrix from 'pcalg' into a 'dagitty' object", "topics": [ "pcalg2dagitty" ] }, { "page": "pcAlgo", "title": "PC-Algorithm [OLD]: Estimate Skeleton or Equivalence Class of a DAG", "topics": [ "pcAlgo", "pcAlgo.Perfect" ] }, { "page": "pcAlgo-class", "title": "Class \"pcAlgo\" of PC Algorithm Results, incl. 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{ "page": "rmvDAG", "title": "Generate Multivariate Data according to a DAG", "topics": [ "rmvDAG" ] }, { "page": "rmvnorm.ivent", "title": "Simulate from a Gaussian Causal Model", "concept": [ "Gaussian causal model" ], "topics": [ "rmvnorm.ivent" ] }, { "page": "Score-class", "title": "Virtual Class \"Score\"", "topics": [ "Score-class" ] }, { "page": "searchAM", "title": "Search for certain nodes in a DAG/CPDAG/MAG/PAG", "topics": [ "searchAM" ] }, { "page": "shd", "title": "Compute Structural Hamming Distance (SHD)", "topics": [ "shd" ] }, { "page": "showAmat", "title": "Show Adjacency Matrix of pcAlgo object", "topics": [ "showAmat" ] }, { "page": "showEdgeList", "title": "Show Edge List of pcAlgo object", "topics": [ "showEdgeList" ] }, { "page": "simy", "title": "Estimate Interventional Markov Equivalence Class of a DAG", "concept": [ "dynamic programming", "interventional Markov equivalence" ], "topics": [ "simy" ] }, { "page": "skeleton", "title": "Estimate (Initial) Skeleton 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