Application to estimate statistical values using properties provided by a group of individuals to describe
  concepts using 'shiny'. It estimates the underlying distribution to generate new descriptive words
  Canessa et al. (2023) <doi:10.3758/s13428-022-01811-w>, applies a new clustering model, and uses simulations to estimate 
  the probability that two persons describe the same words based on their descriptions
  Canessa et al. (2022) <doi:10.3758/s13428-022-02030-z>.

Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) <doi:10.1177/0894439320907027>.

An interface to WordNet using the Jawbone Java API to WordNet.
  WordNet (<https://wordnet.princeton.edu/>) is a large lexical database of
  English.  Nouns, verbs, adjectives and adverbs are grouped into sets of
  cognitive synonyms (synsets), each expressing a distinct concept.  Synsets
  are interlinked by means of conceptual-semantic and lexical relations.
  Please note that WordNet(R) is a registered tradename.  Princeton
  University makes WordNet available to research and commercial users
  free of charge provided the terms of their license
  (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed,
  and proper reference is made to the project using an appropriate
  citation (<https://wordnet.princeton.edu/citing-wordnet>).
  The WordNet database files need to be made available separately,
  either via package 'wordnetDicts' from <https://datacube.wu.ac.at>,
  installing system packages where available, or direct download from
  <https://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>.

Methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks.

Apply 'Wordpiece' (<arXiv:1609.08144>) tokenization to input text, 
 given an appropriate vocabulary. The 'BERT' (<arXiv:1810.04805>) tokenization 
 conventions are used by default.

Provides data to be used by the wordpiece algorithm in order to 
    tokenize text into somewhat meaningful chunks. Included vocabularies were 
    retrieved from 
    <https://huggingface.co/bert-base-cased/resolve/main/vocab.txt> and 
    <https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt> and parsed
    into an R-friendly format.