# Causal Modeling with Coincidence Analysis

## Documentation for package ‘cna’ version 2.1.1

- 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 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
condition | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
condition.condTbl | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
condition.default | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
condTbl | Extract conditions and solutions from an object of class "cna"
cscna | Perform Coincidence Analysis
cscond | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
csf | Extract conditions and solutions from an object of class "cna"
cstt | Assemble cases with identical configurations in a truth table
d.autonomy | Emergence and endurance of autonomy of biodiversity institutions in Costa Rica
d.educate | Artifical data on education levels and left-party strength
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 represention in parliaments of western countries
fscna | Perform Coincidence Analysis
fscond | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
fstt | Assemble cases with identical configurations in a truth table
group.by.outcome | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
makeFuzzy | Generate fuzzy-set data set by simulating noise
minimalizeCsf | Reduce csf by eliminating redundant parts
minimalizeCsf.cna | Reduce csf by eliminating redundant parts
minimalizeCsf.default | Reduce csf by eliminating redundant parts
msc | Extract conditions and solutions from an object of class "cna"
mvcna | Perform Coincidence Analysis
mvcond | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
mvtt | Assemble cases with identical configurations in a truth table
print.cna | Perform Coincidence Analysis
print.cond | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
print.condList | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
print.condTbl | Extract conditions and solutions from an object of class "cna"
print.minimalizeCsf | Reduce csf by eliminating redundant parts
print.truthTab | Assemble cases with identical configurations in a truth table
selectCases | Select the cases/configurations that are compatible with a data generating causal structure
selectCases1 | Select the cases/configurations that are compatible with a data generating causal structure
some | Randomly select configurations from a data frame or 'truthTab'
some.data.frame | Randomly select configurations from a data frame or 'truthTab'
some.truthTab | Randomly select configurations from a data frame or 'truthTab'
summary.condList | Uncover relevant properties of msc, asf, and csf in a data frame or 'truthTab'
truthTab | Assemble cases with identical configurations in a truth table
tt2df | Transform a truth table into a data frame