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pcalg (version 2.7-6)

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

2.7-6

License

GPL (>= 2)

Maintainer

Markus Kalisch

Last Published

April 4th, 2022

Functions in pcalg (2.7-6)

Virtual Class "Score"

GaussL0penIntScore-class

Class "GaussL0penIntScore"

GaussL0penObsScore-class

Class "GaussL0penObsScore"

Class "EssGraph"

Class "ParDAG" of Parametric Causal Models

GaussParDAG-class

Class "GaussParDAG" of Gaussian Causal Models

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

Linear non-Gaussian Acyclic Models (LiNGAM)

Compute adjustment sets for covariate adjustment.

G square Test for (Conditional) Independence of Binary Variables

Add background knowledge to a CPDAG or PDAG

Compare two graphs in terms of TPR, FPR and TDR

Test Conditional Independence of Gaussians via Fisher's Z

Types and Display of Adjacency Matrices in Package 'pcalg'

Convert a DAG to an Essential Graph

beta.special.pcObj

Compute set of intervention effects in a fast way

Convert a DAG with latent variables into a PAG

Compute set of intervention effects

Estimate a PAG with the FCI Algorithm

Find Set Satisfying the Generalized Backdoor Criterion (GBC)

Compute D-SEP(x,y,G)

Check Consistency of Conditional Independence for a Triple of Nodes

G square Test for (Conditional) Independence of Discrete Variables

Estimate a PAG with the FCI+ Algorithm

Plotting a pcAlgo object using the package igraph

Class "gAlgo"

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

Test If Set Satisfies Generalized Adjustment Criterion (GAC)

Test for d-separation in a MAG

Latent Variable 4-Dim Graphical Model Data Example

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

Class "fciAlgo" of FCI Algorithm Results

Computing the correlation graph

Estimate Multiset of Possible Joint Total Causal Effects

Test for d-separation in a DAG

Get the "graph" Part or Aspect of R Object

Graphical Model 7-dim IDA Data Examples

find.unsh.triple

Find all Unshielded Triples in an Undirected Graph

Test for d-separation in a DAG

Convert a DAG to a CPDAG

Get an optimal intervention target

Enumerate All DAGs in a Markov Equivalence Class

Graphical Model 8-Dimensional Interventional Gaussian Example Data

Plot the subgraph around a Specific Node in a Graph Object

Reads off identifiable unconfounded node pairs from a directed PAG

Compute (Large) Correlation Matrix

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

Test for d-separation in a MAG

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

Greedy DAG Search to Estimate Markov Equivalence Class of DAG

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

Internal Pcalg Functions

pcSelect.presel

Estimate Subgraph around a Response Variable using Preselection

Generate Multivariate Data according to a DAG

Estimate an RFCI-PAG using the RFCI Algorithm

Generate a Gaussian Causal Model Randomly

Estimate the Markov equivalence class of a DAG using GES

Check for a DAG, CPDAG or a maximally oriented PDAG

Estimate Multiset of Possible Total Joint Effects

Compute Structural Hamming Distance (SHD)

Show Adjacency Matrix of pcAlgo object

Transform the adjacency matrix from pcalg into a dagitty object

Compute Partial Correlations

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

Random DAG Generation

Generate a Directed Acyclic Graph (DAG) randomly

Compute the optimal adjustment set

Find possible ancestors of given node(s).

Covariance matrix of a DAG.

Estimate Interventional Markov Equivalence Class of a DAG by GIES

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

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

Graphical Model Discrete 5-Dim Example Data

Graphical Model 5-Dim Binary Example Data

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

Graphical Model 8-Dimensional Gaussian Example Data

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

Utility for conservative and majority rule in PC and FCI

Estimate Final Skeleton in the FCI algorithm

Check if a 3-node-path is Legal

Find possible descendants of given node(s).

Estimate Interventional Markov Equivalence Class of a DAG

Check visible edge.

[DEPRECATED] Find possible descendants on definite status paths.

Show Edge List of pcAlgo object

Conversion between an intervention matrix and a list of intervention targets

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

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

PC-Select: Estimate subgraph around a response variable

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

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

Simulate from a Gaussian Causal Model

Plot partial ancestral graphs (PAG)