Quantile Data Envelopment Analysis R package qDEA version 1.0.0 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>. Software Depends: R (>= 4.2) Imports: dplyr, doBy, highs, Matrix, utils Suggests: knitr, rmarkdown Joe Atwood <jatwood@montana.edu> Comprehensive R Archive Network (CRAN) Joe Atwood [aut, cre], John Walden [aut] GPL-2 GPL-3 2026-04-13 application/tgz https://CRAN.R-project.org/package=qDEA doi:10.32614/CRAN.package.qDEA