Implements common geostatistical methods in a clean, straightforward, efficient manner. The methods are discussed in Schabenberger and Gotway (2004, <ISBN:9781584883227>) and Waller and Gotway (2004, <ISBN:9780471387718>).

Evaluate and validate the Geboes score for histological assessment
  of inflammation in ulcerative colitis.  The original Geboes score from Geboes,
  et al. (2000) <doi:10.1136/gut.47.3.404>, binary version from Li, et al.
  (2019) <doi:10.1093/ecco-jcc/jjz022>, and continuous version from Magro, et
  al. (2020) <doi:10.1093/ecco-jcc/jjz123> are all described and implemented.

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>. Unlike 
    traditional methods, '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. 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).