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>.

Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.