Contains functions used across packages 'DDIwR', 'QCA' and 'venn'.
    Interprets and translates, factorizes and negates SOP - Sum of Products
    expressions, for both binary and multi-value crisp sets, and extracts
    information (set names, set values) from those expressions. Other functions
    perform various other checks if possibly numeric (even if all numbers reside
    in a character vector) and coerce to numeric, or check if the numbers are
    whole. It also offers, among many others, a highly versatile recoding
    routine and some more flexible alternatives to the base functions 'with()'
    and 'within()'.
    SOP simplification functions in this package use related minimization from
    package 'QCA', which is recommended to be installed despite not being listed
    in the Imports field, due to circular dependency issues.

Provides functions to perform the fitting of an adaptive mixture
    of Student-t distributions to a target density through its kernel function as described in
    Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The
    mixture approximation can then be used as the importance density in importance
    sampling or as the candidate density in the Metropolis-Hastings algorithm to
    obtain quantities of interest for the target density itself.

Implements techniques to estimate the unknown quantities
    related to two-component admixture models, where the two components
    can belong to any distribution (note that in the case of multinomial
    mixtures, the two components must belong to the same family).
    Estimation methods depend on the assumptions made on the unknown
    component density; see Bordes and Vandekerkhove (2010)
    <doi:10.3103/S1066530710010023>, Patra and Sen (2016)
    <doi:10.1111/rssb.12148>, and Milhaud, Pommeret, Salhi, Vandekerkhove
    (2024) <doi:10.3150/23-BEJ1593>. In practice, one can estimate both
    the mixture weight and the unknown component density in a wide variety
    of frameworks. On top of that, hypothesis tests can be performed in
    one and two-sample contexts to test the unknown component density (see
    Milhaud, Pommeret, Salhi and Vandekerkhove (2022)
    <doi:10.1016/j.jspi.2021.05.010>, and Milhaud, Pommeret, Salhi,
    Vandekerkhove (2024) <doi:10.3150/23-BEJ1593>). Finally, clustering of
    unknown mixture components is also feasible in a K-sample setting (see
    Milhaud, Pommeret, Salhi, Vandekerkhove (2024) 
    <https://jmlr.org/papers/v25/23-0914.html>).