Imports the 'gkmSVM' v2.0 functionalities into R <https://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
    Users should note that the suggested packages 'rtracklayer', 'GenomicRanges', 'BSgenome', 'BiocGenerics', 
    'Biostrings', 'GenomeInfoDb', 'IRanges', and 'S4Vectors' are all BioConductor packages <https://bioconductor.org>.

Kernel regularized least squares, also known as kernel ridge regression, 
    is a flexible machine learning method. This package implements this method by 
    providing a smooth term for use with 'mgcv' and uses random sketching to 
    facilitate scalable estimation on large datasets. It provides additional 
    functions for calculating marginal effects after estimation and for use with 
    ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), 
    and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024)
    <doi:10.1017/pan.2023.27> provide further details.

Implements the five-parameter Generalized Kumaraswamy ('gkw') 
    distribution proposed by 'Carrasco, Ferrari and Cordeiro (2010)' 
    <doi:10.48550/arXiv.1004.0911> and its seven nested sub-families for 
    modeling bounded continuous data on the unit interval (0,1). The 'gkw' 
    distribution extends the Kumaraswamy distribution described by Jones (2009) 
    <doi:10.1016/j.stamet.2008.04.001>. Provides density, distribution, 
    quantile, and random generation functions, along with analytical 
    log-likelihood, gradient, and Hessian functions implemented in 'C++' via 
    'RcppArmadillo' for maximum computational efficiency. Suitable for modeling 
    proportions, rates, percentages, and indices exhibiting complex features 
    such as asymmetry, or heavy tails and other shapes not adequately captured by 
    standard distributions like simple Beta or Kumaraswamy.

Implements regression models for bounded continuous data in the open interval (0,1) 
    using the five-parameter Generalized 'Kumaraswamy' distribution. Supports modeling 
    all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of 
    predictors through various link functions. Provides efficient maximum likelihood 
    estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics,
    model comparison tools, and simulation methods. Particularly useful for analyzing 
    proportions, rates, indices, and other bounded response data with complex 
    distributional features not adequately captured by simpler models.