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pcalg (version 2.7-10)

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

2.7-10

License

GPL (>= 2)

Maintainer

Markus Kalisch

Last Published

February 6th, 2024

Functions in pcalg (2.7-10)

Compute adjustment sets for covariate adjustment.

Test Conditional Independence of Gaussians via Fisher's Z

G square Test for (Conditional) Independence of Binary Variables

Compare two graphs in terms of TPR, FPR and TDR

Check Consistency of Conditional Independence for a Triple of Nodes

Types and Display of Adjacency Matrices in Package 'pcalg'

Find Set Satisfying the Generalized Backdoor Criterion (GBC)

Computing the correlation graph

Compute set of intervention effects

Convert a DAG to a CPDAG

beta.special.pcObj

Compute set of intervention effects in a fast way

G square Test for (Conditional) Independence of Discrete Variables

Test for d-separation in a DAG

Compute D-SEP(x,y,G)

Class "fciAlgo" of FCI Algorithm Results

Test for d-separation in a MAG

Test for d-separation in a MAG

Test for d-separation in a DAG

Convert a DAG to an Essential Graph

Convert a DAG with latent variables into a PAG

Estimate a PAG with the FCI Algorithm

Class "gAlgo"

find.unsh.triple

Find all Unshielded Triples in an Undirected Graph

Estimate a PAG with the FCI+ Algorithm

Test If Set Satisfies Generalized Adjustment Criterion (GAC)

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

Get the "graph" Part or Aspect of R Object

Graphical Model 5-Dim Binary Example Data

Estimate Interventional Markov Equivalence Class of a DAG by GIES

Estimate the Markov equivalence class of a DAG using GES

Greedy DAG Search to Estimate Markov Equivalence Class of DAG

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

Graphical Model 8-Dimensional Interventional Gaussian Example Data

Graphical Model 7-dim IDA Data Examples

Latent Variable 4-Dim Graphical Model Data Example

Estimate Multiset of Possible Total Joint Effects

Graphical Model 8-Dimensional Gaussian Example Data

Graphical Model Discrete 5-Dim Example Data

Estimate Multiset of Possible Joint Total Causal Effects

Check if a 3-node-path is Legal

Reads off identifiable unconfounded node pairs from a directed PAG

Conversion between an intervention matrix and a list of intervention targets

Get an optimal intervention target

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

Compute the optimal adjustment set

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

Transform the adjacency matrix from pcalg into a dagitty object

Compute (Large) Correlation Matrix

Estimate the Equivalence Class of a DAG using the PC Algorithm

Utility for conservative and majority rule in PC and FCI

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

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

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

Compute Partial Correlations

Estimate Final Skeleton in the FCI algorithm

pcSelect.presel

Estimate Subgraph around a Response Variable using Preselection

Internal Pcalg Functions

Plot the subgraph around a Specific Node in a Graph Object

Generate a Gaussian Causal Model Randomly

Random DAG Generation

Plot partial ancestral graphs (PAG)

Find possible ancestors of given node(s).

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

Enumerate All DAGs in a Markov Equivalence Class

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

PC-Select: Estimate subgraph around a response variable

Compute Structural Hamming Distance (SHD)

Generate a Directed Acyclic Graph (DAG) randomly

Simulate from a Gaussian Causal Model

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

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

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

[DEPRECATED] Find possible descendants on definite status paths.

Covariance matrix of a DAG.

Find possible descendants of given node(s).

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

Show Edge List of pcAlgo object

Show Adjacency Matrix of pcAlgo object

Estimate an RFCI-PAG using the RFCI Algorithm

Generate Multivariate Data according to a DAG

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

Estimate Interventional Markov Equivalence Class of a DAG

Class "ParDAG" of Parametric Causal Models

GaussL0penObsScore-class

Class "GaussL0penObsScore"

Linear non-Gaussian Acyclic Models (LiNGAM)

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

GaussParDAG-class

Class "GaussParDAG" of Gaussian Causal Models

Virtual Class "Score"

Add background knowledge to a CPDAG or PDAG

Class "EssGraph"

GaussL0penIntScore-class

Class "GaussL0penIntScore"