Methods for Graphical Models and Causal Inference
Add background knowledge to a CPDAG or PDAG
Compute adjustment sets for covariate adjustment.
Estimate an APDAG within the Markov equivalence class of a DAG using A...
Types and Display of Adjacency Matrices in Package 'pcalg'
Find Set Satisfying the Generalized Backdoor Criterion (GBC)
Compute set of intervention effects in a fast way
Compute set of intervention effects
G square Test for (Conditional) Independence of Binary Variables
Check Consistency of Conditional Independence for a Triple of Nodes
Compare two graphs in terms of TPR, FPR and TDR
Test Conditional Independence of Gaussians via Fisher's Z
Computing the correlation graph
Convert a DAG to a CPDAG
Convert a DAG to an Essential Graph
Convert a DAG with latent variables into a PAG
G square Test for (Conditional) Independence of Discrete Variables
Compute D-SEP(x,y,G)
Test for d-separation in a DAG
Test for d-separation in a MAG
Test for d-separation in a MAG
Test for d-separation in a DAG
Class "EssGraph"
Estimate a PAG with the FCI Algorithm
Class "fciAlgo" of FCI Algorithm Results
Estimate a PAG with the FCI+ Algorithm
Find all Unshielded Triples in an Undirected Graph
Test If Set Satisfies Generalized Adjustment Criterion (GAC)
Class "gAlgo"
Class "GaussL0penIntScore"
Class "GaussL0penObsScore"
Class "GaussParDAG" of Gaussian Causal Models
Greedy DAG Search to Estimate Markov Equivalence Class of DAG
Estimate the Markov equivalence class of a DAG using GES
Get the "graph" Part or Aspect of R Object
Iteration through a list of all combinations of choose(n,k)
Estimate Interventional Markov Equivalence Class of a DAG by GIES
Estimate Multiset of Possible Joint Total Causal Effects
Multiset of Possible Total Causal Effects for Several Target Var.s
Plotting a pcAlgo object using the package igraph
Check for a DAG, CPDAG or a maximally oriented PDAG
Estimate Multiset of Possible Total Joint Effects
Check if a 3-node-path is Legal
Linear non-Gaussian Acyclic Models (LiNGAM)
Conversion between an intervention matrix and a list of intervention t...
Compute (Large) Correlation Matrix
Get an optimal intervention target
Compute the optimal adjustment set
Reads off identifiable ancestors and non-ancestors from a directed PAG
Reads off identifiable unconfounded node pairs from a directed PAG
Reads off identifiable parents and non-parents from a directed PAG
Transform a PAG into a MAG in the Corresponding Markov Equivalence Cla...
Class "ParDAG" of Parametric Causal Models
Utility for conservative and majority rule in PC and FCI
Estimate the Equivalence Class of a DAG using the PC Algorithm
Internal Pcalg Functions
Transform the adjacency matrix from pcalg into a dagitty object
Class "pcAlgo" of PC Algorithm Results, incl. Skeleton
PC-Algorithm [OLD]: Estimate Skeleton or Equivalence Class of a DAG
Compute Partial Correlations
Estimate Subgraph around a Response Variable using Preselection
PC-Select: Estimate subgraph around a response variable
Enumerate All DAGs in a Markov Equivalence Class
Extend a Partially Directed Acyclic Graph (PDAG) to a DAG
Estimate Final Skeleton in the FCI algorithm
Plot partial ancestral graphs (PAG)
Plot the subgraph around a Specific Node in a Graph Object
Find possible ancestors of given node(s).
Find possible descendants of given node(s).
[DEPRECATED] Find possible descendants on definite status paths.
Compute Possible-D-SEP(x,G) of a node x in a PDAG G
Generate a Gaussian Causal Model Randomly
Random DAG Generation
Generate a Directed Acyclic Graph (DAG) randomly
Estimate an RFCI-PAG using the RFCI Algorithm
Generate Multivariate Data according to a DAG
Simulate from a Gaussian Causal Model
Virtual Class "Score"
Search for certain nodes in a DAG/CPDAG/MAG/PAG
Compute Structural Hamming Distance (SHD)
Show Adjacency Matrix of pcAlgo object
Show Edge List of pcAlgo object
Estimate Interventional Markov Equivalence Class of a DAG
Estimate (Initial) Skeleton of a DAG using the PC / PC-Stable Algorith...
Covariance matrix of a DAG.
Last step of RFCI algorithm: Transform partially oriented graph into R...
Last steps of FCI algorithm: Transform Final Skeleton into FCI-PAG
Last PC Algorithm Step: Extend Object with Skeleton to Completed PDAG
Check visible edge.
Weight Matrix of a Graph, e.g., a simulated DAG
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.