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>.

Generalized Entropy Calibration produces calibration weights 
    using generalized entropy as the objective function for optimization. 
    This approach, as implemented in the 'GECal' package, is based on 
    Kwon, Kim, and Qiu (2024) <doi:10.48550/arXiv.2404.01076>. 'GECal' incorporates design weights into the constraints to maintain design consistency, rather than including 
    them in the objective function itself.

Includes a collection of geographical analysis functions aimed primarily at ecology and conservation science studies, allowing processing of both point and raster data. Now integrates SPECTRE (<https://biodiversityresearch.org/spectre/>), a dataset of global geospatial threat data, 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 model 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. (2025).