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.

Contains methods for fitting Generalized Linear Models (GLMs) 
  and Generalized Additive Models (GAMs). Generalized regression models are
  common methods for handling data for which assuming Gaussian-distributed
  errors is not appropriate. For instance, if the response of interest is
  binary, count, or proportion data, one can instead model the expectation of
  the response based on an appropriate data-generating distribution.
  This package provides methods for fitting GLMs and GAMs under
  Beta regression, Poisson regression, Gamma regression, and Binomial regression
  (currently GLM only) settings. Models are fit using local scoring algorithms
  described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.