dagitty0.3-4 package

Graphical Analysis of Structural Causal Models

adjustmentSets

Covariate Adjustment Sets

ancestorGraph

Ancestor Graph

AncestralRelations

Ancestral Relations

as.dagitty

Convert to DAGitty object

backDoorGraph

Back-Door Graph

canonicalize

Canonicalize an Ancestral Graph

completeDAG

Generate Complete DAG

convert

Convert from DAGitty object to other graph types

coordinates

Plot Coordinates of Variables in Graph

dagitty

Parse DAGitty Graph

dconnected

d-Separation

downloadGraph

Load Graph from dagitty.net

edges

Graph Edges

EquivalentModels

Generating Equivalent Models

exogenousVariables

Retrieve Exogenous Variables

getExample

Get Bundled Examples

graphLayout

Generate Graph Layout

graphType

Get Graph Type

impliedConditionalIndependencies

List Implied Conditional Independencies

impliedCovarianceMatrix

Implied Covariance Matrix of a Gaussian Graphical Model

instrumentalVariables

Find Instrumental Variables

is.dagitty

Test for Graph Class

isAcyclic

Test for Cycles

isAdjustmentSet

Adjustment Criterion

isCollider

Test for Colliders

lavaanToGraph

Convert Lavaan Model to DAGitty Graph

localTests

Test Graph against Data

measurementPart

Extract Measurement Part from Structural Equation Model

moralize

Moral Graph

names.dagitty

Names of Variables in Graph

orientPDAG

Orient Edges in PDAG.

paths

Show Paths

plot.dagitty

Plot Graph

plotLocalTestResults

Plot Results of Local Tests

randomDAG

Generate DAG at Random

simulateLogistic

Simulate Binary Data from DAG Structure

simulateSEM

Simulate Data from Structural Equation Model

structuralPart

Extract Structural Part from Structural Equation Model

toMAG

Convert DAG to MAG.

topologicalOrdering

Get Topological Ordering of DAG

vanishingTetrads

List Implied Vanishing Tetrads

VariableStatus

Variable Statuses

A port of the web-based software 'DAGitty', available at <https://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.

  • Maintainer: Johannes Textor
  • License: GPL-2
  • Last published: 2023-12-07