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pcalg (version 2.7-12)

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

12,248

Version

2.7-12

License

GPL (>= 2)

Maintainer

Last Published

September 12th, 2024

Functions in pcalg (2.7-12)

GaussL0penObsScore-class

Class "GaussL0penObsScore"

GaussL0penIntScore-class

Class "GaussL0penIntScore"

GaussParDAG-class

Class "GaussParDAG" of Gaussian Causal Models

Compute set of intervention effects

Computing the correlation graph

Compare two graphs in terms of TPR, FPR and TDR

Virtual Class "Score"

Test Conditional Independence of Gaussians via Fisher's Z

Convert a DAG to a CPDAG

beta.special.pcObj

Compute set of intervention effects in a fast way

Types and Display of Adjacency Matrices in Package 'pcalg'

Estimate a PAG with the FCI Algorithm

Compute adjustment sets for covariate adjustment.

Find Set Satisfying the Generalized Backdoor Criterion (GBC)

Class "fciAlgo" of FCI Algorithm Results

Add background knowledge to a CPDAG or PDAG

Test for d-separation in a DAG

Test for d-separation in a MAG

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

Test for d-separation in a DAG

G square Test for (Conditional) Independence of Discrete Variables

G square Test for (Conditional) Independence of Binary Variables

Check Consistency of Conditional Independence for a Triple of Nodes

Test for d-separation in a MAG

Compute D-SEP(x,y,G)

Convert a DAG to an Essential Graph

Estimate Interventional Markov Equivalence Class of a DAG by GIES

Graphical Model 5-Dim Binary Example Data

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

Convert a DAG with latent variables into a PAG

Greedy DAG Search to Estimate Markov Equivalence Class of DAG

Estimate the Markov equivalence class of a DAG using GES

Plotting a pcAlgo object using the package igraph

Get the "graph" Part or Aspect of R Object

Class "gAlgo"

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

Reads off identifiable unconfounded node pairs from a directed PAG

Graphical Model 7-dim IDA Data Examples

Test If Set Satisfies Generalized Adjustment Criterion (GAC)

Reads off identifiable parents and non-parents from a directed PAG

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

Graphical Model 8-Dimensional Interventional Gaussian Example Data

Check for a DAG, CPDAG or a maximally oriented PDAG

Estimate Multiset of Possible Total Joint Effects

Latent Variable 4-Dim Graphical Model Data Example

PC-Select: Estimate subgraph around a response variable

Estimate Multiset of Possible Joint Total Causal Effects

Compute the optimal adjustment set

Transform the adjacency matrix from pcalg into a dagitty object

Reads off identifiable ancestors and non-ancestors from a directed PAG

Estimate a PAG with the FCI+ Algorithm

Enumerate All DAGs in a Markov Equivalence Class

Compute Partial Correlations

Find possible descendants of given node(s).

find.unsh.triple

Find all Unshielded Triples in an Undirected Graph

Graphical Model Discrete 5-Dim Example Data

[DEPRECATED] Find possible descendants on definite status paths.

Check if a 3-node-path is Legal

Graphical Model 8-Dimensional Gaussian Example Data

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

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

Estimate the Equivalence Class of a DAG using the PC Algorithm

Estimate Final Skeleton in the FCI algorithm

Conversion between an intervention matrix and a list of intervention targets

Random DAG Generation

Plot partial ancestral graphs (PAG)

pcSelect.presel

Estimate Subgraph around a Response Variable using Preselection

Estimate an RFCI-PAG using the RFCI Algorithm

Generate Multivariate Data according to a DAG

Internal Pcalg Functions

Compute (Large) Correlation Matrix

Plot the subgraph around a Specific Node in a Graph Object

Find possible ancestors of given node(s).

Show Edge List of pcAlgo object

Estimate Interventional Markov Equivalence Class of a DAG

Get an optimal intervention target

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

Covariance matrix of a DAG.

Utility for conservative and majority rule in PC and FCI

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

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

Generate a Directed Acyclic Graph (DAG) randomly

Generate a Gaussian Causal Model Randomly

Compute Structural Hamming Distance (SHD)

Show Adjacency Matrix of pcAlgo object

Simulate from a Gaussian Causal Model

Search for certain nodes in a DAG/CPDAG/MAG/PAG

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

Check visible edge.

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

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

Weight Matrix of a Graph, e.g., a simulated DAG

Class "EssGraph"

Linear non-Gaussian Acyclic Models (LiNGAM)

Class "ParDAG" of Parametric Causal Models