Learn R Programming

cna (version 4.0.0)

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 (2020) . 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.

Copy Link

Version

Install

install.packages('cna')

Monthly Downloads

515

Version

4.0.0

License

GPL (>= 2)

Maintainer

Mathias Ambuehl

Last Published

April 4th, 2025

Functions in cna (4.0.0)

Internal functions in the cna package

Create summary tables for conditions

condList-methods

Methods for class “condList”

Calculate the coherence of complex solution formulas

Fine-tuning and modifying the CNA algorithm

Evaluate msc, asf, and csf on the level of cases/configurations in the data

Perform Coincidence Analysis

Extract solutions from an object of class “cna”

Deprecated functions in the cna package

cna: A Package for Causal Modeling with Coincidence Analysis

Detect cyclic substructures in complex solution formulas (csf)

Assemble cases with identical configurations into a configuration table

Job security regulations in western democracies

Transform a configuration table into a data frame

Artificial data on education levels and left-party strength

Data on the voting outcome of the 2009 Swiss Minaret Initiative

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

Emergence and endurance of autonomy of biodiversity institutions in Costa Rica

Artificial data with 50 factors and 1191 cases

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

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

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

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

Party ban provisions in sub-Saharan Africa

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

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

Fuzzifying crisp-set data

Identify correctness-preserving submodel relations

Eliminate logical redundancies from Boolean expressions

print method for an object of class “cna”

Randomly select configurations from a data frame or configTable

Show names and abbreviations of con/cov measures and details

Convert fs data to cs data

Calculate summary measures for msc, asf, and csf

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

Generate random solution formulas