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cna (version 3.6.2)

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

487

Version

3.6.2

License

GPL (>= 2)

Maintainer

Mathias Ambuehl

Last Published

July 5th, 2024

Functions in cna (3.6.2)

Internal functions in the cna package

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

Calculate the coherence of complex solution formulas

condList-methods

Methods for class “condList”

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

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

Perform Coincidence Analysis

cna: A Package for Causal Modeling with Coincidence Analysis

Deprecated functions in the cna package

Assemble cases with identical configurations in a configuration table

Data on the voting outcome of the 2009 Swiss Minaret Initiative

Job security regulations in western democracies

Transform a configuration table into a data frame

Emergence and endurance of autonomy of biodiversity institutions in Costa Rica

Artificial data on education levels and left-party strength

Detect cyclic substructures in complex solution formulas (csf)

Artificial data with 50 factors and 1191 cases

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

Eliminate logical redundancies from Boolean expressions

Fuzzifying crisp-set data

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

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

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

Eliminate structural redundancies from csf

Generate random solution formulas

Randomly select configurations from a data frame or configTable

Identify correctness-preserving submodel relations

Identify structurally redundant asf in a csf

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

Eliminate redundancies from a disjunctive normal form (DNF)

Party ban provisions in sub-Saharan Africa

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

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

Shortcut functions with fixed type argument.