Implements the multivariate classification methods of Alexander
    Alexandrovich Lyubishchev (1890-1972), as described in his 1943 manuscript
    'Programma obshchey sistematiki'
    Lyubishchev (1943) <https://www.zin.ru/animalia/coleoptera/rus/lyubis05.htm>
    and published in Lubischew (1962) <https://www.jstor.org/stable/2527894>.
    Provides divergence_coefficient() for measuring separation between groups
    on continuous features, scatter_ellipse() for fitting covariance ellipses
    per class, transgression() for detecting ellipse overlap, and classify()
    for Bayesian posterior classification. These methods predate and are more
    general than the binary-character similarity coefficients of
    Sokal and Sneath (1963) that appear in other R packages.

An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.

Provides the lzrq() function for estimating
    logarithmic regression slopes in quantile regression models, permitting
    the outcome variable to take on non-positive values. lzrq() conducts regression
    after replacing non-positive values with a sufficiently negative value.
    If the fitted values of a quantile regression on this transformed outcome are all 
    greater than the negative value, then results are displayed. The resulting
    coefficients can be meaningfully interpreted as logarithmic intensive-margin 
    relationships between the outcome variable and the independent variables, even with 
    non-positive values in the outcome variable. If the condition does not
    hold for the specified quantile, then the command iteratively makes the value larger and
    checks again. After ten iterations where the condition does not hold, the functions return
    an error and suppress results. This is an automated adaptation of the algorithm described 
    by Liu & Kaplan (2025) <https://drive.google.com/file/d/1F3dnhm8MrlO5aRrGt48rBWAEaBqdCBH-/view>
    and implemented in the companion 'Stata' command 'lzqreg', described in
    Fitzgerald et al. (2026)
    <doi:10.31222/osf.io/juda7_v1>.