Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the book by C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press and in the online appendix.

Version 2.3.1
o Improved: the output of the theory.evaluation function now also shows percentage of cases in each intersection between empirics and theory that have and don't have the outcome
o Changed: argument "imposs_LR" in function esa() renamed to "untenable_LR"
o Improved: the output for deviant cases coverage sufficiency is now ordered by truth table row.
Version 2.3
o New: new function mmr() replaces all case identification functions (individual functions are still available as single functions in this version) and can be used to select best available cases for single case studies and best pairs of matching cases for comparative case studies
o New: new function cluster() can now return pooled, within, and between consistencies for the relationship between two sets, for an object of class "qca", and for a Boolean expression

o New: function theory.evaluation() can now perform all theory evaluation analyses together, showing membership of cases in the intersections between theory and empirics, the names and proportion of cases that fall in these, and parameters of fit for the various intersections. The output is an object of class "theoryeval"
o New: function cases.suf.typ.fct() embedded in the mmr() function lists typical cases for sufficiency for each focal conjunct, rather than for the whole term
o New: function QCAradar() can now produce radar visualisation for "qca" solution objects and for Boolean expressions
o New: function QCAfit() can now accept "qca" solution objects and truth table rows as input
o Changed: former function theory.evaluation() is now called theory.data()
o Changed: former function cluster.eqmcc() is now called cluster.minimize()
o Changed: argument "intermed" is now deprecated in all the MMR, cluster, theory evaluation functions
o Changed: all the datasets in the package are now renamed in a more simple way
o Improved: argument "sol" can now work with more complex structures of model ambiguity.
o Improved: documentation for various functions
o Fixed: bug in esa() to work with necessary unions of conditions in argument “nec_cond” and to accept more complex expressions involving both unions and intersections

Version 2.1 o New: pimplot() can now plot specific truth table rows with argument “ttrows” o New: pimplot() can now plot all truth table rows above a certain consistency threshold with argument “incl.tt” o New: pimplot() can now plot necessity results obtained from superSubset() in package QCA with argument “necessity” set to TRUE and by specifying the “sS” object in argument “results” o New: function cases.theory.evaluation() now also shows percentages of cases in the various intersections between theory and empirics o Improved: function cases.theory.evaluation() can now be saved in an object of class “casestheoryeval” which allows for a more user-friendly output o Improved: documentation for various functions o Improved: functions QCAfit(), pimplot(), xy.plot(), and xy.plot.lat() also show the new parameter of fit Haesebrouck’s consistency as Cons.Suf(H) o Changed: in function QCAfit() argument “negation” renamed to “neg.out” o Changed: in function QCAfit() the parameter of fit PRODUCT is now optional and can be shown with logical argument “product” o Fixed: esa() can now work with necessary unions of conditions in argument “nec_cond” o Fixed: argument “imposs_LR” in function esa() can now work with intersections of conditions o Fixed: results from matching functions matches.suf.typtyp() and matches.suf.typiir() can now be saved in an object of class “matchessuf” o Fixed: bug in pimdata and functions relying on it, now these also work with intermediate solutions that only have one sufficient term

Version 2.0 o New: functions for MMR that can select single cases for case studies: cases.suf.dcn(), cases.suf.dcv(), cases.suf.iir(), cases.suf.typ(), cases.suf.typ.most(), cases.suf.typ.unique() o New: functions for MMR that can select pairs of cases for comparative case studies: matches.suf.typdcn(), matches.suf.dcviir() o New: functions for MMR that can select pairs of cases based on the INUS principles; matches.suf.typtyp() matches typical cases for each conjunct in a sufficient term and matches.suf.typiir() matches typical and individually irrelevant cases for each conjunct in a sufficient term o New: datasets Schneider and SchneiderLong are added to the package o New: new function cluster.diagnostics() can perform parameter of diagnostics for clustered data in the relationship between two single conditions o New: new function cluster.eqmcc() can perform parameter of diagnostics for clustered data for analyses of sufficiency using results obtained from the eqmcc() function in package QCA o New: new function theory.evaluation() shows membership of cases in the intersections between a theory specified in boolean terms and an empirical solution obtained from the eqmcc()function in package QCA o New: new function theory.fit() shows parameters of fit for the intersections between theory and empirical results using the object produced by the function theory.evaluation() o New: new function cases.theory.evaluation() shows the names of cases in the intersections between theory and empirical results using the object produced by the function theory.evaluation() o New: new function esa() performs Enhanced Standard Analysis and returns a new truth table in which all truth table rows are set to outcome value 0 that would otherwise present untenable assumptions. o Improved: documentation for all functions across the package o Improved: all MMR functions across the package have follow improved formulas and have an improved output o Fixed: bugs in various functions across the package