Estimates Pareto-Optimal Solution for Hiring with 3 Objectives R package rMOST version 1.0.1 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). Software Imports: graphics, grDevices, nloptr, stats Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Chelsea Song <qianqisong@gmail.com> Comprehensive R Archive Network (CRAN) Chelsea Song [aut, cre] (ORCID: <https://orcid.org/0000-0003-0910-1281>), Yesuel Kim [ctb] (ORCID: <https://orcid.org/0000-0002-8486-7693>) MIT + file LICENSE (https://CRAN.R-project.org/package=rMOST/LICENSE) 2023-11-08 application/tgz https://CRAN.R-project.org/package=rMOST doi:10.32614/CRAN.package.rMOST