Package: dagitty 0.3-4

dagitty: Graphical Analysis of Structural Causal Models

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.

Authors:Johannes Textor, Benito van der Zander, Ankur Ankan

dagitty_0.3-4.tar.gz
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dagitty_0.3-4.tgz(r-4.6-any)dagitty_0.3-4.tgz(r-4.5-any)
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dagitty_0.3-4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
dagitty/json (API)
NEWS

# Install 'dagitty' in R:
install.packages('dagitty', repos = c('https://jtextor.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jtextor/dagitty/issues

On CRAN:

Conda:

13.56 score 341 stars 18 packages 2.5k scripts 34k downloads 8 mentions 60 exports 6 dependencies

Last updated from:7a657776dc. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK144
source / vignettesOK158
linux-release-x86_64OK130
macos-release-arm64OK208
macos-oldrel-arm64OK288
windows-develOK410
windows-releaseOK427
windows-oldrelOK410
wasm-releaseOK109

Exports:adjacentNodesadjustedNodesadjustedNodes<-adjustmentSetsancestorGraphancestorsas.dagittybackDoorGraphcanonicalizechildrenciTestcompleteDAGconvertcoordinatescoordinates<-dagittydconnecteddescendantsdownloadGraphdseparatededgesequivalenceClassequivalentDAGsexogenousVariablesexposuresexposures<-findCyclegetExamplegraphLayoutgraphTypeimpliedConditionalIndependenciesimpliedCovarianceMatrixinstrumentalVariablesis.dagittyisAcyclicisAdjustmentSetisColliderlatentslatents<-lavaanToGraphlocalTestsmarkovBlanketmeasurementPartmoralizeneighboursorientPDAGoutcomesoutcomes<-parentspathsplotLocalTestResultsrandomDAGsetVariableStatussimulateLogisticsimulateSEMspousesstructuralParttoMAGtopologicalOrderingvanishingTetrads

Dependencies:bootcurljsonliteMASSRcppV8

A SEM user's guide to dagitty for R

Johannes Textor

Rendered fromdagitty4semusers.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2016-03-22
Started: 2016-03-11

Readme and manuals

Help Manual

Help pageTopics
Covariate Adjustment SetsadjustmentSets
Ancestor GraphancestorGraph
Ancestral RelationsadjacentNodes ancestors AncestralRelations children descendants markovBlanket neighbours parents spouses
Convert to DAGitty objectas.dagitty
Back-Door GraphbackDoorGraph
Canonicalize an Ancestral Graphcanonicalize
Generate Complete DAGcompleteDAG
Convert from DAGitty object to other graph typesconvert
Plot Coordinates of Variables in Graphcoordinates coordinates<-
Parse DAGitty Graphdagitty
d-Separationdconnected dseparated
Load Graph from dagitty.netdownloadGraph
Graph Edgesedges
Generating Equivalent ModelsequivalenceClass equivalentDAGs EquivalentModels
Retrieve Exogenous VariablesexogenousVariables
Get Bundled ExamplesgetExample
Generate Graph LayoutgraphLayout
Get Graph TypegraphType
List Implied Conditional IndependenciesimpliedConditionalIndependencies
Implied Covariance Matrix of a Gaussian Graphical ModelimpliedCovarianceMatrix
Find Instrumental VariablesinstrumentalVariables
Test for Graph Classis.dagitty
Test for CyclesfindCycle isAcyclic
Adjustment CriterionisAdjustmentSet
Test for CollidersisCollider
Convert Lavaan Model to DAGitty GraphlavaanToGraph
Test Graph against DataciTest localTests
Extract Measurement Part from Structural Equation ModelmeasurementPart
Moral Graphmoralize
Names of Variables in Graphnames.dagitty
Orient Edges in PDAG.orientPDAG
Show Pathspaths
Plot Graphplot.dagitty
Plot Results of Local TestsplotLocalTestResults
Generate DAG at RandomrandomDAG
Simulate Binary Data from DAG StructuresimulateLogistic
Simulate Data from Structural Equation ModelsimulateSEM
Extract Structural Part from Structural Equation ModelstructuralPart
Convert DAG to MAG.toMAG
Get Topological Ordering of DAGtopologicalOrdering
List Implied Vanishing TetradsvanishingTetrads
Variable StatusesadjustedNodes adjustedNodes<- exposures exposures<- latents latents<- outcomes outcomes<- setVariableStatus VariableStatus