Package: cna 3.6.2

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 (2018) <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.

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

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cna/json (API)
NEWS

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

Peer review:

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:

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

4.91 score 3 packages 45 scripts 471 downloads 5 mentions 63 exports 61 dependencies

Last updated 5 months agofrom:6e12bae715. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-win-x86_64OKNov 03 2024
R-4.5-linux-x86_64OKNov 03 2024
R-4.4-win-x86_64OKNov 03 2024
R-4.4-mac-x86_64OKNov 03 2024
R-4.4-mac-aarch64OKNov 03 2024
R-4.3-win-x86_64OKNov 03 2024
R-4.3-mac-x86_64OKNov 03 2024
R-4.3-mac-aarch64OKNov 03 2024

Exports:allCombsas.condTblasfC_is_submodelC_mconcatC_recCharList2charC_redundC_relist_IntcnacoherenceconditioncondTblconfigTablecscnacscondcsctcsfcsttct2dfctInfocyclicextract_asffs2csfscnafscondfsctfsttfull.ctfull.ttgetComplexitygetCondgetCondTypegroup.by.outcomehstrsplitidentical.modelis.inusis.submodellhsmakeFuzzymatchCondminimalizeminimalizeCsfmscmvcnamvcondmvctmvttnoblanksqcond_asfqcond_boolqcond_csfrandomAsfrandomCsfredundantrelist1rhsrreduceselectCasesselectCases1somestdCondtruthTabtt2df

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmatrixStatsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpurrrquantregR6RColorBrewerRcppRcppEigenrlangscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Introduction to the CNA method and package

Rendered fromcna.Rnwusingutils::Sweaveon Nov 03 2024.

Last update: 2024-07-06
Started: 2021-05-28

Readme and manuals

Help Manual

Help pageTopics
cna: A Package for Causal Modeling with Coincidence Analysiscna-package
Generate all logically possible value configurations of a given set of factorsallCombs
Perform Coincidence Analysiscna print.cna
Calculate the coherence of complex solution formulascoherence coherence.cti coherence.default
Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'condition condition.condTbl condition.default print.cond print.condList
Methods for class "condList"as.data.frame.condList condList-methods group.by.outcome summary.condList
Extract conditions and solutions from an object of class "cna"as.condTbl as.data.frame.condTbl asf condTbl csf msc print.condTbl
Assemble cases with identical configurations in a configuration tableconfigTable print.configTable
Transform a configuration table into a data framect2df
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
Generate the logically possible value configurations of a given set of factorsfull.ct 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
Eliminate structural redundancies from csfminimalizeCsf minimalizeCsf.cna minimalizeCsf.default
Generate random solution formulasrandomAsf randomConds randomCsf
Identify structurally redundant asf in a csfredundant
Eliminate redundancies from a disjunctive normal form (DNF)rreduce
Select the cases/configurations compatible with a data generating causal structureselectCases selectCases1
Randomly select configurations from a data frame or 'configTable'some some.configTable some.data.frame