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pcalg (version 2.6-9)

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

1,818

Version

2.6-9

License

GPL (>= 2)

Maintainer

Markus Kalisch

Last Published

February 11th, 2020

Functions in pcalg (2.6-9)

Class "ParDAG" of Parametric Causal Models

GaussL0penObsScore-class

Class "GaussL0penObsScore"

Add background knowledge to a CPDAG or PDAG

Virtual Class "Score"

GaussL0penIntScore-class

Class "GaussL0penIntScore"

Compute adjustment sets for covariate adjustment.

GaussParDAG-class

Class "GaussParDAG" of Gaussian Causal Models

Class "EssGraph"

G square Test for (Conditional) Independence of Binary Variables

Types and Display of Adjacency Matrices in Package 'pcalg'

Linear non-Gaussian Acyclic Models (LiNGAM)

Computing the correlation graph

Test for d-separation in a DAG

Convert a DAG to a CPDAG

beta.special.pcObj

Compute set of intervention effects in a fast way

Compute set of intervention effects

Check Consistency of Conditional Independence for a Triple of Nodes

find.unsh.triple

Find all Unshielded Triples in an Undirected Graph

Compare two graphs in terms of TPR, FPR and TDR

Convert a DAG to an Essential Graph

Estimate a PAG by the FCI+ Algorithm

Test Conditional Independence of Gaussians via Fisher's Z

Convert a DAG with latent variables into a PAG

Find Set Satisfying the Generalized Backdoor Criterion (GBC)

G square Test for (Conditional) Independence of Discrete Variables

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

Test for d-separation in a DAG

Get the "graph" Part or Aspect of R Object

Class "gAlgo"

Compute D-SEP(x,y,G)

Class "fciAlgo" of FCI Algorithm Results

Greedy DAG Search to Estimate Markov Equivalence Class of DAG

Check for a DAG, CPDAG or a maximally oriented PDAG

Latent Variable 4-Dim Graphical Model Data Example

Estimate Interventional Markov Equivalence Class of a DAG by GIES

Test If Set Satisfies Generalized Adjustment Criterion (GAC)

Estimate a PAG by the FCI Algorithm

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

Graphical Model Discrete 5-Dim Example Data

Graphical Model 8-Dimensional Gaussian Example Data

Plotting a pcAlgo object using the package igraph

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

Estimate Multiset of Possible Joint Total Causal Effects

Get an optimal intervention target

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

Graphical Model 7-dim IDA Data Examples

Check if a 3-node-path is Legal

Compute the optimal adjustment set

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

Graphical Model 5-Dim Binary Example Data

Compute (Large) Correlation Matrix

Estimate the Markov equivalence class of a DAG using GES

Estimate Multiset of Possible Total Joint Effects

Graphical Model 8-Dimensional Interventional Gaussian Example Data

Conversion between an intervention matrix and a list of intervention targets

Find possible descendants of given node(s).

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

PC-Select: Estimate subgraph around a response variable

Estimate the Equivalence Class of a DAG using the PC Algorithm

Find possible ancestors of given node(s).

Transform the adjacency matrix from pcalg into a dagitty object

Random DAG Generation

Compute Partial Correlations

Estimate Final Skeleton in the FCI algorithm

Generate a Gaussian Causal Model Randomly

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

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

pcSelect.presel

Estimate Subgraph around a Response Variable using Preselection

Enumerate All DAGs in a Markov Equivalence Class

Internal Pcalg Functions

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

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

Utility for conservative and majority rule in PC and FCI

[DEPRECATED] Find possible descendants on definite status paths.

Plot the subgraph around a Specific Node in a Graph Object

Generate Multivariate Data according to a DAG

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

Simulate from a Gaussian Causal Model

Covariance matrix of a DAG.

Show Edge List of pcAlgo object

Compute Structural Hamming Distance (SHD)

Generate a Directed Acyclic Graph (DAG) randomly

Show Adjacency Matrix of pcAlgo object

Estimate an RFCI-PAG using the RFCI Algorithm

Plot partial ancestral graphs (PAG)

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

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

Estimate Interventional Markov Equivalence Class of a DAG

Check visible edge.