Provides a unified analytical workflow that bridges conventional
    binary and multinomial logistic regression with singly-ordered (SONSCA)
    and doubly-ordered (DONSCA) nonsymmetric correspondence analysis.
    Log-odds ratios (LORs) from logistic regression are re-expressed as
    cosine theta estimates and closeness-of-concordance measures (CCMs) --
    including Yule's Q, Yule's Y, and r_meta -- on the familiar [-1, +1]
    scale introduced by Kim and Grochowalski (2019)
    <doi:10.1007/s00357-018-9277-7>. Bootstrap confidence intervals for
    cosine theta are provided throughout. The package is intended to help
    clinical and medical researchers interpret association strength from
    logistic regression in an intuitive, correlation-like metric, and to
    connect conventional regression results with geometric correspondence
    analysis visualisations.

Performs analysis of Differential Item Functioning (DIF) for
        dichotomous and polytomous items using an iterative hybrid of
        ordinal logistic regression and item response theory (IRT) according to 
        Choi, Gibbons, and Crane (2011) <doi:10.18637/jss.v039.i08>.

Estimate covariance matrices that contain low rank and sparse components

Quickly generate lorem ipsum placeholder text. Easy to
    integrate in RMarkdown documents. Includes an RStudio addin to insert
    lorem ipsum into the current document.

The Lorentz transformation in special relativity; also the
    gyrogroup structure of three-velocities.  Performs active and
    passive transformations and has the ability to use units in which the
    speed of light is not unity.  Includes some experimental
    functionality for celerity and rapidity.  For general relativity,
    see the 'schwarzschild' package.

Inference for the Lorenz and penalized Lorenz regressions. More broadly, the package proposes functions to assess inequality and graphically represent it. The Lorenz Regression procedure is introduced in Heuchenne and Jacquemain (2022) <doi:10.1016/j.csda.2021.107347> and in Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024) <doi:10.1214/23-EJS2200>.