Automatic normalisation of a data frame to third normal form, with
  the intention of easing the process of data cleaning. (Usage to design your
  actual database for you is not advised.)
  Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx'
  (<https://github.com/alteryx/autonormalize>), with various changes and
  improvements. Automatic discovery of functional or approximate dependencies,
  normalisation based on those, and plotting of the resulting "database" via
  'Graphviz', with options to exclude some attributes at discovery time, or
  remove discovered dependencies at normalisation time.

An interface to the 'AutoDesk' Platform Services ('APS') 'API' including
    the Authentication 'API' for obtaining 'OAuth2' tokens with expiry tracking, Data
    Management 'API' for managing buckets and objects across the platform's cloud
    services, Design Automation 'API' for running automated tasks on design files in
    the cloud, Model Derivative 'API' for translating design files into 'SVF', 'SVF2',
    'OBJ', and 'STL' formats and extracting model data, Reality Capture 'API' for
    generating 3D models from photogrammetry image sets, and Viewer for rendering 2D
    and 3D models in 'Shiny' applications.

A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

Automated data quality auditing using unsupervised machine learning.
    Provides AI-driven anomaly detection for data quality assessment, primarily
    designed for Electronic Health Records (EHR) data, with benchmarking
    capabilities for validation and publication. Methods based on: Liu et al.
    (2008) <doi:10.1109/ICDM.2008.17>, Breunig et al. (2000)
    <doi:10.1145/342009.335388>.