Provides a 'shiny' web application developed by the Breeding Insight team
            to support pedigree validation and ancestry assessment of plant and animal
            populations. The app integrates Mendelian error analysis, parentage assignment
            and genetic composition/ancestry methods to help researchers evaluate genomic
            relationships through an accessible, web-based interface without requiring
            command-line tools. Pedigree validation, Mendelian error analysis and
            parentage assignment build on the 'BIGpopA' package
            (<https://CRAN.R-project.org/package=BIGpopA>). Ancestry estimation uses
            the sparse non-negative matrix
            factorization method of Frichot et al. (2014) <doi:10.1534/genetics.113.160572>
            as implemented in the 'LEA' package by Frichot and Francois (2015)
            <doi:10.1111/2041-210X.12382>. Line and breed composition are
            estimated using the breed composition regression method of Funkhouser
            et al. (2017) <doi:10.2527/tas2016.0003>, extended to polyploid species
            by Sandercock et al. (2025) <doi:10.1002/tpg2.70067>.

Provides functionality for testing familial hypotheses. Supports testing centers 
    belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and 
    two-sample tests are supported, as are directional tests. Methods for visualizing output are 
    provided.

Single unified interface for end-to-end modelling of regression, 
    categorical and time-to-event (survival) outcomes. Models created using
    familiar are self-containing, and their use does not require additional
    information such as baseline survival, feature clustering, or feature
    transformation and normalisation parameters. Model performance,
    calibration, risk group stratification, (permutation) variable importance,
    individual conditional expectation, partial dependence, and more, are
    assessed automatically as part of the evaluation process and exported in
    tabular format and plotted, and may also be computed manually using export
    and plot functions. Where possible, metrics and values obtained during the
    evaluation process come with confidence intervals.