# Causal Modeling with Coincidence Analysis

## Documentation for package ‘cna’ version 3.3.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

cna-package | cna: A Package for Causal Modeling with Coincidence Analysis
allCombs | Generate all logically possible value configurations of a given set of factors
as.condTbl | Extract conditions and solutions from an object of class "cna"
asf | Extract conditions and solutions from an object of class "cna"
cna | Perform Coincidence Analysis
coherence | Calculate the coherence of complex solution formulas
coherence.cti | Calculate the coherence of complex solution formulas
coherence.default | Calculate the coherence of complex solution formulas
condition | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'
condition.condTbl | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'
condition.default | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'
condTbl | Extract conditions and solutions from an object of class "cna"
configTable | Assemble cases with identical configurations in a configuration table
csf | Extract conditions and solutions from an object of class "cna"
ct2df | Transform a configuration table into a data frame
cyclic | Detect cyclic substructures in complex solution formulas (csf)
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
full.ct | Generate all logically possible value configurations of a given set of factors
full.ct.configTable | Generate all logically possible value configurations of a given set of factors
full.ct.cti | Generate all logically possible value configurations of a given set of factors
full.ct.default | Generate all logically possible value configurations of a given set of factors
group.by.outcome | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'
identical.model | Identify correctness-preserving submodel relations
is.inus | Check whether expressions in the syntax of CNA solutions have INUS form
is.submodel | Identify correctness-preserving submodel relations
makeFuzzy | Fuzzifying crisp-set data
minimalize | Eliminate logical redundancies from Boolean expressions
minimalizeCsf | Eliminate structural redundancies from csf
minimalizeCsf.cna | Eliminate structural redundancies from csf
minimalizeCsf.default | Eliminate structural redundancies from csf
msc | Extract conditions and solutions from an object of class "cna"
print.cna | Perform Coincidence Analysis
print.cond | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'
print.condList | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'
print.condTbl | Extract conditions and solutions from an object of class "cna"
print.configTable | Assemble cases with identical configurations in a configuration table
randomAsf | Generate random solution formulas
randomConds | Generate random solution formulas
randomCsf | Generate random solution formulas
redundant | Identify structurally redundant asf in a csf
rreduce | Eliminate redundancies from a disjunctive normal form (DNF)
selectCases | Select the cases/configurations compatible with a data generating causal structure
selectCases1 | Select the cases/configurations compatible with a data generating causal structure
some | Randomly select configurations from a data frame or 'configTable'
some.configTable | Randomly select configurations from a data frame or 'configTable'
some.data.frame | Randomly select configurations from a data frame or 'configTable'
summary.condList | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable'