| cna-package | cna: A Package for Causal Modeling with Coincidence Analysis |
| allCombs | Generate all logically possible value configurations of a given set of factors |
| as.condTbl | Extract conditions and solutions from an object of class "cna" |
| asf | Extract conditions and solutions from an object of class "cna" |
| cna | Perform Coincidence Analysis |
| coherence | Calculate the coherence of complex solution formulas |
| coherence.cti | Calculate the coherence of complex solution formulas |
| coherence.default | Calculate the coherence of complex solution formulas |
| condition | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable' |
| condition.condTbl | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable' |
| condition.default | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable' |
| condTbl | Extract conditions and solutions from an object of class "cna" |
| configTable | Assemble cases with identical configurations in a configuration table |
| csf | Extract conditions and solutions from an object of class "cna" |
| ct2df | Transform a configuration table into a data frame |
| cyclic | Detect cyclic substructures in complex solution formulas (csf) |
| d.autonomy | Emergence and endurance of autonomy of biodiversity institutions in Costa Rica |
| d.educate | Artificial data on education levels and left-party strength |
| d.highdim | Artificial data with 50 factors and 1191 cases |
| d.irrigate | Data on the impact of development interventions on water adequacy in Nepal |
| d.jobsecurity | Job security regulations in western democracies |
| d.minaret | Data on the voting outcome of the 2009 Swiss Minaret Initiative |
| d.pacts | Data on the emergence of labor agreements in new democracies between 1994 and 2004 |
| d.pban | Party ban provisions in sub-Saharan Africa |
| d.performance | Data on combinations of industry, corporate, and business-unit effects |
| d.volatile | Data on the volatility of grassroots associations in Norway between 1980 and 2000 |
| d.women | Data on high percentage of women's representation in parliaments of western countries |
| full.ct | Generate all logically possible value configurations of a given set of factors |
| full.ct.configTable | Generate all logically possible value configurations of a given set of factors |
| full.ct.cti | Generate all logically possible value configurations of a given set of factors |
| full.ct.default | Generate all logically possible value configurations of a given set of factors |
| group.by.outcome | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable' |
| identical.model | Identify correctness-preserving submodel relations |
| is.inus | Check whether expressions in the syntax of CNA solutions have INUS form |
| is.submodel | Identify correctness-preserving submodel relations |
| makeFuzzy | Fuzzifying crisp-set data |
| minimalize | Eliminate logical redundancies from Boolean expressions |
| minimalizeCsf | Eliminate structural redundancies from csf |
| minimalizeCsf.cna | Eliminate structural redundancies from csf |
| minimalizeCsf.default | Eliminate structural redundancies from csf |
| msc | Extract conditions and solutions from an object of class "cna" |
| print.cna | Perform Coincidence Analysis |
| print.cond | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable' |
| print.condList | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable' |
| print.condTbl | Extract conditions and solutions from an object of class "cna" |
| print.configTable | Assemble cases with identical configurations in a configuration table |
| randomAsf | Generate random solution formulas |
| randomConds | Generate random solution formulas |
| randomCsf | Generate random solution formulas |
| redundant | Identify structurally redundant asf in a csf |
| rreduce | Eliminate redundancies from a disjunctive normal form (DNF) |
| selectCases | Select the cases/configurations compatible with a data generating causal structure |
| selectCases1 | Select the cases/configurations compatible with a data generating causal structure |
| some | Randomly select configurations from a data frame or 'configTable' |
| some.configTable | Randomly select configurations from a data frame or 'configTable' |
| some.data.frame | Randomly select configurations from a data frame or 'configTable' |
| summary.condList | Uncover relevant properties of msc, asf, and csf in a data frame or 'configTable' |