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>.

Adversarial random forests (ARFs) recursively partition data into 
    fully factorized leaves, where features are jointly independent. The 
    procedure is iterative, with alternating rounds of generation and 
    discrimination. Data becomes increasingly realistic at each round, until 
    original and synthetic samples can no longer be reliably distinguished. 
    This is useful for several unsupervised learning tasks, such as density
    estimation and data synthesis. Methods for both are implemented in this
    package. ARFs naturally handle unstructured data with mixed continuous and 
    categorical covariates. They inherit many of the benefits of random forests, 
    including speed, flexibility, and solid performance with default parameters. 
    For details, see Watson et al. (2023) <https://proceedings.mlr.press/v206/watson23a.html>.