Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report).

Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.

This package provides different specifications of a Quadrilateral
    Dissimilarity Model which can be used to fit same-different judgments
    in order to get a predicted matrix that satisfies regular minimality
    [Colonius & Dzhafarov, 2006, Measurement and representations of
    sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
    computed.

The letters 'qe' in the package title stand for "quick and
   easy," alluding to the convenience goal of the package. We bring
   together a variety of machine learning (ML) tools from standard R
   packages, providing wrappers with a simple, convenient, 
   and uniform interface.

Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.

The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.

Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. 
 Spline quantile regression (SQR) for regression coefficient estimation.
 References:
    [1] Li, T.-H. (2012) "Quantile periodograms," Journal of the American Statistical
        Association, 107, 765–776, <doi:10.1080/01621459.2012.682815>.
    [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154>
    [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric
        estimation of quantile spectra," <doi:10.48550/arXiv.2211.05844>.
    [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression
         estimation," <doi:10.48550/arXiv.2412.02513>.
    [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum,"
        <doi:10.48550/arXiv.2412.17163>.
    [6] Li, T.-H., and Megiddo, N. (2025) "Spline quantile regression," 
        <doi:10.48550/arXiv.2501.03883>.