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cna (version 3.5.6)

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

641

Version

3.5.6

License

GPL (>= 2)

Maintainer

Mathias Ambuehl

Last Published

December 21st, 2023

Functions in cna (3.5.6)

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

Calculate the coherence of complex solution formulas

condList-methods

Methods for class “condList”

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

cna: A Package for Causal Modeling with Coincidence Analysis

Assemble cases with identical configurations in a configuration table

Internal functions in the cna package

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

Deprecated functions in the cna package

Perform Coincidence Analysis

Detect cyclic substructures in complex solution formulas (csf)

Transform a configuration table into a data frame

Party ban provisions in sub-Saharan Africa

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

Artificial data with 50 factors and 1191 cases

Artificial data on education levels and left-party strength

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

Emergence and endurance of autonomy of biodiversity institutions in Costa Rica

Job security regulations in western democracies

Data on the voting outcome of the 2009 Swiss Minaret Initiative

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

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

Generate random solution formulas

Eliminate structural redundancies from csf

Identify correctness-preserving submodel relations

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

Fuzzifying crisp-set data

Eliminate logical redundancies from Boolean expressions

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

Randomly select configurations from a data frame or configTable

Shortcut functions with fixed type argument.

Eliminate redundancies from a disjunctive normal form (DNF)

Identify structurally redundant asf in a csf

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

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