An interface for data processing, building models, predicting values and analysing outcomes. Fitting Linear Models, Robust Fitting of Linear Models, k-Nearest Neighbor Classification, 1-Nearest Neighbor Classification, and Conditional Inference Trees are available.

Provides tools for detecting, quantifying, and visualizing
    algorithmic bias as a longitudinal process in repeated decision systems.
    Existing fairness metrics treat bias as a single-period snapshot; this
    package operationalizes the view that bias in sequential systems must be
    measured over time. Implements group-specific decision-rate trajectories,
    standardized disparity measures analogous to the standardized mean
    difference (Cohen, 1988, ISBN:0-8058-0283-5), cumulative bias burden,
    Markov-based transition disparity (recovery and retention gaps), and a
    dynamic amplification index that quantifies whether prior decisions
    compound current group inequality. The amplification framework extends
    longitudinal causal inference ideas from Robins (1986)
    <doi:10.1016/0270-0255(86)90088-6> and the sequential decision-process
    perspective in the fairness literature (see <https://fairmlbook.org>)
    to the audit setting. Covariate-adjusted trajectories are estimated via
    logistic regression, generalized additive models (Wood, 2017,
    <doi:10.1201/9781315370279>), or generalized linear mixed models
    (Bates, 2015, <doi:10.18637/jss.v067.i01>). Uncertainty quantification
    uses the cluster bootstrap (Cameron, 2008, <doi:10.1162/rest.90.3.414>).

A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>),  compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).