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).

R implementation of Quantile Data Envelopment Analysis. 
 The package 'qDEA' allows a user specified proportion of observations to lie external to a given
 Decision Making Units's (DMU's)reference hyperplane. 'qDEA' can be used to detect and
 address influential outliers or to implement quantile benchmarking, as discussed in Atwood and
 Shaik (2020). 
 Quantile benchmarking is accomplished by using heuristic procedures to find a DMU's closest
 input-output projection point in a specified direction while allowing a specified 
 proportion of observations to lie external to the projected point's hyperplane. 
 The 'qDEA' package accommodates standard (DEA) and quantile DEA estimation, 
 returns to scale CRS(constant),VRS(variable),DRS(decreasing) or IRS(increasing),
 the use of directional vectors, bias correction through subsample bootstrapping and subsample size 
 selection procedures. The user can also recover each DMU's reference DMUs and external DMUs if desired.
 The implemented procedures are based on discussions in: 
 Atwood and Shaik (2020) <doi:10.1016/j.ejor.2020.03.054>
 Atwood and Shaik (2018) <doi:10.1007/978-3-319-68678-3_4>
 Walden and Atwood (2023) <doi:10.1086/724932>
 Walden and Atwood (2025) <doi:10.1086/736554>.

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.