Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling,
    data resampling, gap filler of precipitation, bias correction of forecasting
    data, flexible time series plot, and spatial map generation. It is a good pre-
    processing and post-processing tool for hydrological and hydraulic modellers.

Facilitates the analysis and evaluation of hydrologic model output and 
    time-series data with functions focused on comparison of modeled (simulated) and observed data, 
    period-of-record statistics, and trends.

High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.

Generalized additive models with a numeric hyper column
       tabulated on a common grid. Sign-adjustment based on the
       correlation of model prediction and a selected slice of the
       hyper column. Visualization of the integrand surface over the
       hyper column.

A suite of routines for the hyperdirichlet distribution
   and reified Bradley-Terry; supersedes the 'hyperdirichlet' package;
   uses 'disordR' discipline <doi:10.48550/ARXIV.2210.03856>.  To cite
   in publications please use Hankin 2017 <doi:10.32614/rj-2017-061>,
   and for Generalized Plackett-Luce likelihoods use Hankin 2024
   <doi:10.18637/jss.v109.i08>.

Implements Data Envelopment Analysis (DEA) with a hyperbolic orientation using a non-linear programming solver. It enables flexible estimations with weight restrictions, non-discretionary variables, and a generalized distance function. Additionally, it allows for the calculation of slacks and super-efficiency scores. The methods are detailed in Öttl et al. (2023), <doi:10.1016/j.dajour.2023.100343>. Furthermore, the package provides a non-linear profitability estimation built upon the DEA framework.