A modern view on the principal component analysis biplot with 
    calibrated axes. Create principal component analysis biplots rendered in 
    HTML with significant reactivity embedded within the plot. Furthermore, 
    the traditional biplot view is enhanced by translated axes with inter-class
    kernel densities superimposed. For more information on biplots, see 
    Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0).

A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.

Provides users with an EZ-to-use platform for representing data
             with biplots. Currently principal component analysis (PCA), canonical variate
             analysis (CVA) and simple correspondence analysis (CA) biplots are included. 
             This is accompanied by various formatting
             options for the samples and axes. Alpha-bags and concentration ellipses
             are included for visual enhancements and interpretation. For an extensive
             discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J.
             (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.

Provides a GUI with which users can construct and interact
        with biplots.

Implements methods for fitting logistic biplot models to
    multivariate binary data. The logistic biplot represents individuals
    as points and binary variables as directed vectors in a low-dimensional
    subspace; the orthogonal projection of each individual onto a variable
    vector approximates the expected probability that the corresponding
    characteristic is present. Available fitting methods include conjugate
    gradient algorithms, a coordinate descent Majorization-Minimization
    (MM) algorithm, and a block coordinate descent algorithm based on data
    projection that supports matrices with missing values and allows new
    individuals to be projected as supplementary rows without refitting
    the model. A cross-validation procedure is provided to select the
    number of latent dimensions k.
    References: Babativa-Marquez and Vicente-Villardon (2021)
    <doi:10.3390/math9162015>;
    Vicente-Villardon and Galindo (2006, ISBN:9780470973196).