A pipeable, transparent implementation of areal weighted interpolation
    with support for interpolating multiple variables in a single function call.
    These tools provide a full-featured workflow for validation and estimation
    that fits into both modern data management (e.g. tidyverse) and spatial 
    data (e.g. sf) frameworks.

Many relevant applications in the environmental and socioeconomic 
    sciences use areal data, such as biodiversity checklists, agricultural statistics, 
    or socioeconomic surveys. For applications that surpass the spatial, temporal or 
    thematic scope of any single data source, data must be integrated from several 
    heterogeneous sources. Inconsistent concepts, definitions, or messy data tables 
    make this a tedious and error-prone process. 'arealDB' tackles those problems and 
    helps the user to integrate a harmonised databases of areal data. Read the paper
    at Ehrmann, Seppelt & Meyer (2020) <doi:10.1016/j.envsoft.2020.104799>.

Plot stacked areas and confidence bands as filled polygons, or add
  polygons to existing plots. A variety of input formats are supported,
  including vectors, matrices, data frames, formulas, etc.

Generates data for challenging machine learning models in 'Arena'
    <https://arena.drwhy.ai> - an interactive web application. You can start
    the server with XAI (Explainable Artificial Intelligence) plots to be
    generated on-demand or precalculate and auto-upload data file beside
    shareable 'Arena' URL.

A Python based pipeline for extraction of species occurrence data through the usage of large language models. Includes validation tools designed to handle model hallucinations for a scientific, rigorous use of LLM. Currently supports usage of GPT with more planned, including local and non-proprietary models. For more details on the methodology used please consult the references listed under each function, such as Kent, A. et al. (1995) <doi:10.1002/asi.5090060209>, van Rijsbergen, C.J. (1979, ISBN:978-0408709293, Levenshtein, V.I. (1966) <https://nymity.ch/sybilhunting/pdf/Levenshtein1966a.pdf> and Klaus Krippendorff (2011) <https://repository.upenn.edu/handle/20.500.14332/2089>.