Functions for fitting and working with generalized	additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).

Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of 'gam()' and 'bam()' in 'mgcv' package, applying the algorithm in this paper: Lai(2024) <doi:10.1016/j.pld.2024.06.002>.

Data sets and scripts used in the book 'Generalized Additive 
             Models: An Introduction with R', Wood (2006,2017) CRC.

Fits unimodal and multimodal gambin distributions to species-abundance distributions
    from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. 
    'gambin' is short for 'gamma-binomial'. The main function is fit_abundances(), which estimates 
    the 'alpha' parameter(s) of the gambin distribution using maximum likelihood. Functions are 
    also provided to generate the gambin distribution and for calculating likelihood statistics.

Boosting models for fitting generalized additive models for
  location, shape and scale ('GAMLSS') to potentially high dimensional
  data.

Functions and data are provided that support a course
        that emphasizes statistical issues of inference and generalizability.  
        The functions are designed to make it straightforward to illustrate
        the use of cross-validation, the training/test approach, simulation, 
        and model-based estimates of accuracy.  Methods considered are
        Generalized Additive Modeling, Linear and Quadratic Discriminant
        Analysis, Tree-based methods, and Random Forests.

Implementation of various inference and simulation tools to
    apply generalized additive models to bivariate dependence structures and
    non-simplified vine copulas.

Implements the GAMbag, GAMrsm and GAMens ensemble
    classifiers for binary classification (De Bock et al., 2010) <doi:10.1016/j.csda.2009.12.013>. The ensembles
    implement Bagging (Breiman, 1996) <doi:10.1023/A:1010933404324>, the Random Subspace Method (Ho, 1998) <doi:10.1109/34.709601>
    , or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.