Causal Modeling with Coincidence Analysis
cna: A Package for Causal Modeling with Coincidence Analysis
Perform Coincidence Analysis
Party ban provisions in sub-Saharan Africa
Generate random solution formulas
Identify structurally redundant asf in a csf
Generate all logically possible value configurations of a given set of...
Deprecated functions in the cna package
Internal functions in the cna package
Calculate the coherence of complex solution formulas
Uncover relevant properties of msc, asf, and csf in a data frame or `c...
Methods for class condList
Extract conditions and solutions from an object of class cna
Assemble cases with identical configurations in a configuration table
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 the voting outcome of the 2009 Swiss Minaret Initiative
Data on the emergence of labor agreements in new democracies between 1...
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...
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
Fuzzifying crisp-set data
Eliminate logical redundancies from Boolean expressions
Eliminate structural redundancies from csf
Eliminate redundancies from a disjunctive normal form (DNF)
Select the cases/configurations compatible with a data generating caus...
Shortcut functions with fixed type argument.
Randomly select configurations from a data frame or configTable
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.