Functions for the g-and-k and generalised g-and-h distributions.

Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.

A simple way to interact with and extract data from
  the official 'Google Knowledge Graph' API <https://developers.google.com/knowledge-graph/>.

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.