rMOST: Estimates Pareto-Optimal Solution for Hiring with 3 Objectives

Estimates Pareto-optimal solution for personnel selection with 3 objectives using Normal Boundary Intersection (NBI) algorithm introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>. Takes predictor intercorrelations and predictor-objective relations as input and generates a series of solutions containing predictor weights as output. Accepts between 3 and 10 selection predictors. Maximum 2 objectives could be adverse impact objectives. Partially modeled after De Corte (2006) TROFSS Fortran program <https://users.ugent.be/~wdecorte/trofss.pdf> and updated from 'ParetoR' package described in Song et al. (2017) <doi:10.1037/apl0000240>. For details, see Study 3 of Zhang et al. (2023).

Version: 1.0.1
Imports: graphics, grDevices, nloptr, stats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-11-08
Author: Chelsea Song ORCID iD [aut, cre], Yesuel Kim ORCID iD [ctb]
Maintainer: Chelsea Song <qianqisong at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: rMOST citation info
Materials: NEWS
CRAN checks: rMOST results

Documentation:

Reference manual: rMOST.pdf
Vignettes: rMOST-vignette

Downloads:

Package source: rMOST_1.0.1.tar.gz
Windows binaries: r-devel: rMOST_1.0.1.zip, r-release: rMOST_1.0.1.zip, r-oldrel: rMOST_1.0.1.zip
macOS binaries: r-release (arm64): rMOST_1.0.1.tgz, r-oldrel (arm64): rMOST_1.0.1.tgz, r-release (x86_64): rMOST_1.0.1.tgz
Old sources: rMOST archive

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