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pcalg (version 2.7-8)

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

1,575

Version

2.7-8

License

GPL (>= 2)

Maintainer

Markus Kalisch

Last Published

December 21st, 2022

Functions in pcalg (2.7-8)

Class "EssGraph"

GaussL0penIntScore-class

Class "GaussL0penIntScore"

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

GaussParDAG-class

Class "GaussParDAG" of Gaussian Causal Models

Add background knowledge to a CPDAG or PDAG

Virtual Class "Score"

Compute set of intervention effects

beta.special.pcObj

Compute set of intervention effects in a fast way

Class "ParDAG" of Parametric Causal Models

Types and Display of Adjacency Matrices in Package 'pcalg'

Test Conditional Independence of Gaussians via Fisher's Z

Compare two graphs in terms of TPR, FPR and TDR

Linear non-Gaussian Acyclic Models (LiNGAM)

G square Test for (Conditional) Independence of Binary Variables

Check Consistency of Conditional Independence for a Triple of Nodes

Convert a DAG to a CPDAG

Test for d-separation in a DAG

Computing the correlation graph

Find Set Satisfying the Generalized Backdoor Criterion (GBC)

Estimate a PAG with the FCI Algorithm

Test for d-separation in a MAG

Class "fciAlgo" of FCI Algorithm Results

Convert a DAG to an Essential Graph

G square Test for (Conditional) Independence of Discrete Variables

Compute D-SEP(x,y,G)

Graphical Model 5-Dim Binary Example Data

Estimate Interventional Markov Equivalence Class of a DAG by GIES

Graphical Model 8-Dimensional Gaussian Example Data

Graphical Model Discrete 5-Dim Example Data

Greedy DAG Search to Estimate Markov Equivalence Class of DAG

Estimate the Markov equivalence class of a DAG using GES

Convert a DAG with latent variables into a PAG

Estimate a PAG with the FCI+ Algorithm

find.unsh.triple

Find all Unshielded Triples in an Undirected Graph

Test for d-separation in a MAG

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

Compute the optimal adjustment set

Check for a DAG, CPDAG or a maximally oriented PDAG

Test for d-separation in a DAG

Class "gAlgo"

Graphical Model 7-dim IDA Data Examples

Test If Set Satisfies Generalized Adjustment Criterion (GAC)

Graphical Model 8-Dimensional Interventional Gaussian Example Data

PC-Select: Estimate subgraph around a response variable

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

Latent Variable 4-Dim Graphical Model Data Example

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

Estimate Multiset of Possible Joint Total Causal Effects

Show Edge List of pcAlgo object

Estimate Interventional Markov Equivalence Class of a DAG

Generate a Gaussian Causal Model Randomly

Check visible edge.

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

Get the "graph" Part or Aspect of R Object

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

Get an optimal intervention target

Compute (Large) Correlation Matrix

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

Utility for conservative and majority rule in PC and FCI

Find possible descendants of given node(s).

[DEPRECATED] Find possible descendants on definite status paths.

Generate Multivariate Data according to a DAG

Estimate an RFCI-PAG using the RFCI Algorithm

Plotting a pcAlgo object using the package igraph

Estimate Multiset of Possible Total Joint Effects

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

Check if a 3-node-path is Legal

Conversion between an intervention matrix and a list of intervention targets

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

Reads off identifiable unconfounded node pairs from a directed PAG

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

Enumerate All DAGs in a Markov Equivalence Class

Estimate the Equivalence Class of a DAG using the PC Algorithm

pcSelect.presel

Estimate Subgraph around a Response Variable using Preselection

Internal Pcalg Functions

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

Plot the subgraph around a Specific Node in a Graph Object

Estimate Final Skeleton in the FCI algorithm

Plot partial ancestral graphs (PAG)

Generate a Directed Acyclic Graph (DAG) randomly

Random DAG Generation

Transform the adjacency matrix from pcalg into a dagitty object

Compute Partial Correlations

Find possible ancestors of given node(s).

Simulate from a Gaussian Causal Model

Compute Structural Hamming Distance (SHD)

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

Show Adjacency Matrix of pcAlgo object

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

Covariance matrix of a DAG.

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

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

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

GaussL0penObsScore-class

Class "GaussL0penObsScore"

Compute adjustment sets for covariate adjustment.