More comfortable interface to work with R data or source files
    in a key-value fashion.

An implementation of the fair data adaptation with quantile
    preservation described in Plecko & Meinshausen (2019) <arXiv:1911.06685>.
    The adaptation procedure uses the specified causal graph to pre-process the
    given training and testing data in such a way to remove the bias caused by
    the protected attribute. The procedure uses tree ensembles for quantile
    regression.

We provide here tools used by the Solar Durability and Lifetime Extension Center (SDLE)
    for FAIRifying data from materials science projects. Functions have been created for numerous tools common
    in the field in order to make the metadata more Findable, Accessible, Interoperable, and Reproducible.

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>".

Fair machine learning regression models which take sensitive attributes into account in
  model estimation. Currently implementing Komiyama et al. (2018) 
  <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>, Zafar et al.
  (2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own
  approach from Scutari, Panero and Proissl (2022)
  <https://link.springer.com/content/pdf/10.1007/s11222-022-10143-w.pdf>
  that uses ridge regression to enforce fairness.

Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021)) <arXiv:2104.00507>.