Tools for training and analysing fairness-aware gated neural 
    networks for subgroup-aware prediction and interpretation in clinical datasets. 
    Methods draw on prior work in mixture-of-experts neural networks by
    Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>,
    fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>,
    and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) 
    <doi:10.1016/j.jpsychires.2016.03.016>.

Translates several CSV files with ontological terms and corresponding
    data into RDF triples. These RDF triples are stored in OWL and JSON-LD files,
    facilitating data accessibility, interoperability, and knowledge unification.
    The triples are also visualized in a graph saved as an SVG. The input CSVs
    must be formatted with a template from a public Google Sheet; see README or
    vignette for more information. This is a tool is used by the SDLE Research
    Center at Case Western Reserve University to create and visualize material
    science ontologies, and it includes example ontologies to demonstrate its
    capabilities. This work was supported by the U.S. Department of Energy’s
    Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy
    Technologies Office (SETO) Agreement Numbers E-EE0009353 and DE-EE0009347,
    Department of Energy (National Nuclear Security Administration) under Award
    Number DE-NA0004104 and Contract number B647887, and U.S. National Science
    Foundation Award under Award Number 2133576.

Clustering for categorical and mixed-type of data, to preventing classification biases due to race, 
            gender or others sensitive attributes.
            This algorithm is an extension of the methodology proposed by "Santos & Heras (2020) <doi:10.28945/4643>".

A collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2024) <doi:10.48550/arXiv.2406.09307>.