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>); Milhaud, Pommeret, Salhi and Vandekerkhove 
    (2022) <doi:10.1016/j.jspi.2021.05.010>). 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, Vandekerkhove (2023)).
    Finally, clustering of unknown mixture components is also feasible in a K-samples setting.

An interface for performing all stages of 'ADMIXTOOLS' analyses
    (<https://reich.hms.harvard.edu/software>) entirely from R. Wrapper functions
    (D, f4, f3, etc.) completely automate the generation of intermediate
    configuration files, run 'ADMIXTOOLS' programs on the command-line, and
    parse output files to extract values of interest. This allows users to focus
    on the analysis itself instead of worrying about low-level technical details.
    A set of complementary functions for processing and filtering of data in
    the 'EIGENSTRAT' format is also provided.

Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM).
    See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.