Causal Modeling with Coincidence Analysis
Deprecated functions in the cna package
Internal functions in the cna package
cna: A Package for Causal Modeling with Coincidence Analysis
Extract solutions from an object of class cna
Perform Coincidence Analysis
Fine-tuning and modifying the CNA algorithm
Calculate the coherence of complex solution formulas
Evaluate msc, asf, and csf on the level of cases/configurations in the...
Methods for class condList
Create summary tables for conditions
Assemble cases with identical configurations into a configuration tabl...
Data on the voting outcome of the 2009 Swiss Minaret Initiative
Data on the emergence of labor agreements in new democracies between 1...
Party ban provisions in sub-Saharan Africa
Fuzzifying crisp-set data
Eliminate logical redundancies from Boolean expressions
print method for an object of class cna
Generate random solution formulas
Select the cases/configurations compatible with a data generating caus...
Show names and abbreviations of con/cov measures and details
Randomly select configurations from a data frame or configTable
Transform a configuration table into a data frame
Detect cyclic substructures in complex solution formulas (csf)
Emergence and endurance of autonomy of biodiversity institutions in Co...
Artificial data on education levels and left-party strength
Data on the impact of development interventions on water adequacy in N...
Job security regulations in western democracies
Data on combinations of industry, corporate, and business-unit effects
Data on the volatility of grassroots associations in Norway between 19...
Data on high percentage of women's representation in parliaments of we...
Calculate summary measures for msc, asf, and csf
Convert fs data to cs data
Generate the logically possible value configurations of a given set of...
Check whether expressions in the syntax of CNA solutions have INUS for...
Identify correctness-preserving submodel relations
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.