Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.

Provides tools for the quantitative analysis of axon integrity in microscopy images. 
  It implements image pre-processing, adaptive thresholding, feature extraction, and support vector machine-based classification to compute indices such as the Axon Integrity Index (AII) and Degeneration Index (DI).
  The package is designed for reproducible and automated analysis in neuroscience research.

In social and educational settings, the use of Artificial
    Intelligence (AI) is a challenging task. Relevant data is often only
    available in handwritten forms, or the use of data is restricted by
    privacy policies. This often leads to small data sets. Furthermore, in
    the educational and social sciences, data is often unbalanced in terms
    of frequencies. To support educators as well as educational and social
    researchers in using the potentials of AI for their work, this package
    provides a unified interface for neural nets in 'PyTorch' to deal with
    natural language problems. In addition, the package ships with a shiny
    app, providing a graphical user interface.  This allows the usage of
    AI for people without skills in writing python/R scripts.  The tools
    integrate existing mathematical and statistical methods for dealing
    with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al.
    (2020) <doi:10.48550/arXiv.2001.06001>) and imbalanced data via the
    creation of synthetic cases (e.g.  Islam et al. (2012)
    <doi:10.1016/j.asoc.2021.108288>).  Performance evaluation of AI is
    connected to measures from content analysis which educational and
    social researchers are generally more familiar with (e.g. Berding &
    Pargmann (2022) <doi:10.30819/5581>, Gwet (2014)
    <ISBN:978-0-9708062-8-4>, Krippendorff (2019)
    <doi:10.4135/9781071878781>). Estimation of energy consumption and CO2
    emissions during model training is done with the 'python' library
    'codecarbon'.  Finally, all objects created with this package allow to
    share trained AI models with other people.