An extensive set of functions to perform Qualitative Comparative Analysis:
       crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA')
       and fuzzy sets ('fsQCA'), using a GUI - graphical user interface.
       'QCA' is a methodology that bridges the qualitative and quantitative
       divide in social science research. It uses a Boolean minimization
       algorithm, resulting in a minimal causal configuration associated
       with a given phenomenon.

Clustered set-relational data in Qualitative Comparative Analysis
    (QCA) can have a hierarchical structure, a panel structure or repeated cross
    sections. 'QCAcluster' allows QCA researchers to supplement the analysis
    of pooled the data with a disaggregated perspective focusing on selected 
    partitions of the data. The pooled data can be partitioned along the 
    dimensions of the clustered data (individual cross sections or time series) 
    to perform partition-specific truth table minimizations. Empirical 
    researchers can further calculate the weight that each partition has on the 
    parameters of the pooled solution and the diversity of the cases under 
    analysis within and across partitions 
    (see <https://ingorohlfing.github.io/QCAcluster/>).

Researchers working with Qualitative Comparative Analysis (QCA)
    can use the package to estimate power of a sufficient term
    using permutation tests. A term can be anything: A condition, conjunction or 
    disjunction of any combination of these. The package further allows users to plot 
    the estimation results and to estimate the number of cases required to achieve a
    certain level of power, given a prespecified null and alternative hypothesis.
    Reference for the article introducing power estimation for QCA is: 
    Rohlfing, Ingo (2018) <doi:10.1017/pan.2017.30> 
    (ungated version: <doi:10.17605/OSF.IO/PC4DF>).

Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.