Package: cna 4.0.3

cna: Causal Modeling with Coincidence Analysis

Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2020) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures. Moreover, as of version 4.0, it is the only method of its kind that provides measures for model evaluation and selection that are custom-made for the problem of INUS-discovery.

Authors:Mathias Ambuehl [aut, cre, cph], Michael Baumgartner [aut, cph], Ruedi Epple [ctb], Veli-Pekka Parkkinen [ctb], Alrik Thiem [ctb]

cna_4.0.3.tar.gz
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cna_4.0.3.tgz(r-4.6-x86_64)cna_4.0.3.tgz(r-4.6-arm64)cna_4.0.3.tgz(r-4.5-x86_64)cna_4.0.3.tgz(r-4.5-arm64)
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cna_4.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
cna/json (API)
NEWS

# Install 'cna' in R:
install.packages('cna', repos = c('https://mathiasambuehl.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • d.autonomy - Emergence and endurance of autonomy of biodiversity institutions in Costa Rica
  • d.educate - Artificial data on education levels and left-party strength
  • d.highdim - Artificial data with 50 factors and 1191 cases
  • d.irrigate - Data on the impact of development interventions on water adequacy in Nepal
  • d.jobsecurity - Job security regulations in western democracies
  • d.minaret - Data on the voting outcome of the 2009 Swiss Minaret Initiative
  • d.pacts - Data on the emergence of labor agreements in new democracies between 1994 and 2004
  • d.pban - Party ban provisions in sub-Saharan Africa
  • d.performance - Data on combinations of industry, corporate, and business-unit effects
  • d.volatile - Data on the volatility of grassroots associations in Norway between 1980 and 2000
  • d.women - Data on high percentage of women's representation in parliaments of western countries

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

4.36 score 1 stars 3 packages 51 scripts 446 downloads 5 mentions 66 exports 70 dependencies

Last updated from:96968ff47a. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK233
linux-devel-x86_64OK208
source / vignettesOK290
linux-release-arm64OK201
linux-release-x86_64OK209
macos-release-arm64OK210
macos-release-x86_64OK394
macos-oldrel-arm64OK180
macos-oldrel-x86_64OK432
windows-develOK217
windows-releaseOK191
windows-oldrelOK213
wasm-releaseOK169

Exports:.det.inusallCombsas.condTblasfC_concatC_is_submodelC_mconcatC_recCharList2charC_redundC_relist_IntcnacnaControlcoherenceconditioncondListcondTblconfigTablecscnacscondcsctcsfct2dfctInfocyclicdetailMeasuresextract_asffs2csfscnafscondfsctfull.ctgetComplexitygetCondgetCondTypegroup.by.outcomehstrsplitidentical.modelis.inusis.submodellhsmakeFuzzymatchCondminimalizeminimalizeCsfmscmvcnamvcondmvctnoblanksqcond_asfqcond_boolqcond_csfrandomAsfrandomCsfredundantrelist1rhsrreduceselectCasesselectCases1showConCovMeasuresshowDetailMeasuresshowMeasuressomestdCond

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfarverforecastFormulafracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4lmtestmagrittrMASSMatrixMatrixModelsmatrixStatsmgcvmicrobenchmarkminqamodelrnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7scalesSparseMstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrzoo

Introduction to the CNA method and package

Rendered fromcna.Rnwusingutils::Sweaveon May 28 2026.

Last update: 2025-04-04
Started: 2021-05-28

Readme and manuals

Help Manual

Help pageTopics
cna: A Package for Causal Modeling with Coincidence Analysiscna-package
Perform Coincidence Analysiscna
Extract solutions from an object of class "cna"asf cna-solutions csf msc
Fine-tuning and modifying the CNA algorithmcnaControl
Calculate the coherence of complex solution formulascoherence coherence.cti coherence.default
Evaluate msc, asf, and csf on the level of cases/configurations in the datacondition condList condList.character condList.condTbl print.cond print.condList
Methods for class "condList"as.data.frame.condList condList-methods group.by.outcome summary.condList
Create summary tables for conditionsas.condTbl as.data.frame.condTbl condTbl print.condTbl
Assemble cases with identical configurations into a configuration tableconfigTable print.configTable
Transform a configuration table into a data frameas.data.frame.configTable ct2df
Detect cyclic substructures in complex solution formulas (csf)cyclic
Emergence and endurance of autonomy of biodiversity institutions in Costa Ricad.autonomy
Artificial data on education levels and left-party strengthd.educate
Artificial data with 50 factors and 1191 casesd.highdim
Data on the impact of development interventions on water adequacy in Nepald.irrigate
Job security regulations in western democraciesd.jobsecurity
Data on the voting outcome of the 2009 Swiss Minaret Initiatived.minaret
Data on the emergence of labor agreements in new democracies between 1994 and 2004d.pacts
Party ban provisions in sub-Saharan Africad.pban
Data on combinations of industry, corporate, and business-unit effectsd.performance
Data on the volatility of grassroots associations in Norway between 1980 and 2000d.volatile
Data on high percentage of women's representation in parliaments of western countriesd.women
Calculate summary measures for msc, asf, and csfdetailMeasures
Convert fs data to cs datafs2cs
Generate the logically possible value configurations of a given set of factorsallCombs full.ct full.ct.condTbl full.ct.configTable full.ct.cti full.ct.default
Check whether expressions in the syntax of CNA solutions have INUS formis.inus
Identify correctness-preserving submodel relationsidentical.model is.submodel
Fuzzifying crisp-set datamakeFuzzy
Eliminate logical redundancies from Boolean expressionsminimalize
'print' method for an object of class "cna"print.cna
Generate random solution formulasrandomAsf randomConds randomCsf
Select the cases/configurations compatible with a data generating causal structureselectCases selectCases1
Show names and abbreviations of con/cov measures and detailsshowConCovMeasures showDetailMeasures showMeasures
Randomly select configurations from a data frame or 'configTable'some some.configTable some.data.frame