A collection of regular expression tools associated with
        the 'qdap' package that may be useful outside of the context of
        discourse analysis. Tools include
        removal/extraction/replacement of abbreviations, dates, dollar
        amounts, email addresses, hash tags, numbers, percentages,
        citations, person tags, phone numbers, times, and zip codes.

A collection of tools associated with the 'qdap' package that may be useful outside of the
            context of text analysis.

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>.