pcalg v2.6-7
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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 | |
| 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 | |
| No Results! | ||
Vignettes of pcalg
| Name | ||
| Figure1FAT.pdf | ||
| Figure2FAT.pdf | ||
| Mybib.bib | ||
| Mybibliography.bib | ||
| jsslogo.jpg | ||
| pcalgDoc.Rnw | ||
| vignette2018.Rnw | ||
| No Results! | ||
Last month downloads
Details
| Date | 2019-10-20 |
| 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 | 2019-10-23 10:20:32 UTC; kalischm |
| Repository | CRAN |
| Date/Publication | 2019-10-23 14:40:02 UTC |
| imports | abind , bdsmatrix , clue , corpcor , fastICA , ggm , graph , graphics , igraph , methods , RBGL , Rcpp , robustbase , sfsmisc (>= 1.0-26) , stats , utils , vcd |
| linkingto | BH , RcppArmadillo |
| suggests | dagitty , ggplot2 , huge , MASS , Matrix , mvtnorm , Rgraphviz |
| depends | R (>= 3.0.2) |
| Contributors | Doris Entner, Patrik Hoyer, Antti Hyttinen, Jonas Peters, Diego Colombo, Alain Hauser, Martin Maechler, Daniel Stekhoven, Nicoletta Andri, Emilija Perkovic, Preetam Nandy, Philipp Ruetimann, Manuel Schuerch, Marco Eigenmann |
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