Generates reproducible random samples from any user-specified
    univariate distribution under a comprehensive suite of censoring and
    truncation schemes.
    Users supply the probability density function (PDF),
    cumulative distribution function (CDF), survival function, support bounds,
    and parameters; the same seed and inputs yield identical samples across
    sessions.  Supported schemes include right and left truncation, random,
    right, left, interval, and middle censoring, block random censoring,
    balanced joint progressive Type-II (BJPT-II), progressive first failure,
    joint Type-I, Type-I, Type-II, progressive Type-II, Type-II progressively
    hybrid, joint Type-II, hybrid, hybrid Type-I, doubly Type-II, Type-I
    hybrid, and hybrid Type-II censoring.  Diagnostic histogram, dot plot, and
    autocorrelation plots are provided for each scheme to verify distributional
    behaviour.
    Methods are described in
    Nagar, Kumar, and Krishna (2026) <doi:10.59467/IJASS.2026.22.1>,
    Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions
    using balanced joint progressively Type-II censored data"),
    Wu and Kus (2009) <doi:10.1016/j.csda.2009.03.010>,
    Goel and Krishna (2026) <doi:10.1007/s13198-026-03208-w>,
    Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5),
    Mondal and Kundu (2020) <doi:10.1080/03610926.2018.1554128>,
    Ding and Gui (2023) <doi:10.3390/math11092003>,
    Prajapati, Mitra, and Kundu (2019) <doi:10.1007/s13571-018-0167-0>,
    Yadav, Jaiswal, and Yadav (2026) <doi:10.1007/s11135-026-02647-8>,
    Iyer, Jammalamadaka, and Kundu (2008) <doi:10.1016/j.jspi.2007.03.062>,
    Banerjee and Kundu (2008) <doi:10.1109/TR.2008.916890>,
    and Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>.

UNIfied Cross-Omics deconvolution (Unico) deconvolves
    standard 2-dimensional bulk matrices of samples by features into a
    3-dimensional tensors representing samples by features by cell types.
    Unico stands out as the first principled model-based deconvolution
    method that is theoretically justified for any heterogeneous genomic
    data. For more details see Chen and Rahmani et al. (2024)
    <doi:10.1101/2024.01.27.577588>.