User-friendly maximum likelihood estimation (Fisher (1921) 
    <doi:10.1098/rsta.1922.0009>) of univariate densities.

Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them.
    The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage).
    The details of the indices in this package can be found in:
    C. Alok. (2010) <https://hdl.handle.net/10603/93443>,
    J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>,
    T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>,
    C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>,
    D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>,
    J. C. Dunn (1973) <doi:10.1080/01969727308546046>,
    F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>,
    M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>,
    S. H. Kwon (1998) <doi:10.1049/EL:19981523>,
    S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>,
    G. W. Miligan (1980) <doi:10.1007/BF02293907>,
    M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>,
    M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>,
    S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>,
    A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>,
    Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>,
    N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>,
    N. Wiroonsri, O. Preedasawakul (2023) <arXiv:2308.14785>,
    C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495> and
    X. Xie, G. Beni (1991) <doi:10.1109/34.85677>.

Provides S3 generic methods and some default implementations
    for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC)
    samples.  The purpose of 'universals' is to reduce package
    dependencies and conflicts.  The 'nlist' package implements many of
    the methods for its 'nlist' class.