Contains the framework of the estimation, sampling, and hypotheses testing for two special distributions (Exponentiated Exponential-Pareto and Exponentiated Inverse Gamma-Pareto) within the family of Generalized Exponentiated Composite distributions. The detailed explanation and the applications of these two distributions were introduced in Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.1080/03610926.2022.2050399>, Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/math10111895>, and Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/app13010645>.

Includes a collection of geographical analysis functions aimed primarily at ecology and conservation science studies, allowing processing of both point and raster data. Future versions will integrate species threat datasets developed by the authors.

Spline Regression, Generalized Additive Models, and Component-wise Gradient
  Boosting, utilizing Geometrically Designed (GeD) Splines. GeDS regression is a
  non-parametric method inspired by geometric principles, for fitting spline regression
  models with variable knots in one or two independent variables. It efficiently estimates
  the number of knots and their positions, as well as the spline order, assuming the
  response variable follows a distribution from the exponential family. GeDS models
  integrate the broader category of Generalized (Non-)Linear Models, offering a flexible
  approach to modeling complex relationships. A description of the method can be found in
  Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023)
  <doi:10.1016/j.amc.2022.127493>. Further extending its capabilities, GeDS's implementation
  includes Generalized Additive Models (GAM) and Functional Gradient Boosting (FGB),
  enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of
  Dimitrova et al. (2024).

Provides adaptive association tests for SNP level, gene level and pathway level analyses.

Estimation of generalized linear models with
    correlated/clustered observations by use of generalized estimating
    equations (GEE). See e.g. Halekoh and Højsgaard, (2005,
    <doi:10.18637/jss.v015.i02>), for details. Several types of
    clustering are supported, including exchangeable variance
    structures, AR1 structures, M-dependent, user-specified variance
    structures and more. The model fitting computations are performed
    using modified code from the 'geeM' package, while the interface
    and output objects have been written to resemble the 'geepack'
    package. The package also contains additional tools for working
    with and inspecting results from the 'geepack' package, e.g. a
    'confint' method for 'geeglm' objects from 'geepack'.