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cna (version 3.6.2)

Causal Modeling with Coincidence Analysis

Description

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) , and generalized in Baumgartner & Ambuehl (2018) . 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.

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Version

Install

install.packages('cna')

Monthly Downloads

518

Version

3.6.2

License

GPL (>= 2)

Maintainer

Last Published

July 5th, 2024

Functions in cna (3.6.2)

cna-internals

Internal functions in the cna package
condTbl

Extract conditions and solutions from an object of class “cna”
coherence

Calculate the coherence of complex solution formulas
condList-methods

Methods for class “condList”
allCombs

Generate all logically possible value configurations of a given set of factors
condition

Uncover relevant properties of msc, asf, and csf in a data frame or configTable
cna

Perform Coincidence Analysis
cna-package

cna: A Package for Causal Modeling with Coincidence Analysis
cna-deprecated

Deprecated functions in the cna package
configTable

Assemble cases with identical configurations in a configuration table
d.minaret

Data on the voting outcome of the 2009 Swiss Minaret Initiative
d.jobsecurity

Job security regulations in western democracies
ct2df

Transform a configuration table into a data frame
d.autonomy

Emergence and endurance of autonomy of biodiversity institutions in Costa Rica
d.educate

Artificial data on education levels and left-party strength
cyclic

Detect cyclic substructures in complex solution formulas (csf)
d.highdim

Artificial data with 50 factors and 1191 cases
d.irrigate

Data on the impact of development interventions on water adequacy in Nepal
minimalize

Eliminate logical redundancies from Boolean expressions
makeFuzzy

Fuzzifying crisp-set data
d.volatile

Data on the volatility of grassroots associations in Norway between 1980 and 2000
d.performance

Data on combinations of industry, corporate, and business-unit effects
is.inus

Check whether expressions in the syntax of CNA solutions have INUS form
minimalizeCsf

Eliminate structural redundancies from csf
randomConds

Generate random solution formulas
some

Randomly select configurations from a data frame or configTable
is.submodel

Identify correctness-preserving submodel relations
redundant

Identify structurally redundant asf in a csf
d.pacts

Data on the emergence of labor agreements in new democracies between 1994 and 2004
rreduce

Eliminate redundancies from a disjunctive normal form (DNF)
d.pban

Party ban provisions in sub-Saharan Africa
d.women

Data on high percentage of women's representation in parliaments of western countries
full.ct

Generate the logically possible value configurations of a given set of factors
selectCases

Select the cases/configurations compatible with a data generating causal structure
shortcuts

Shortcut functions with fixed type argument.