Package: rMOST 1.0.1

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).

Authors:Chelsea Song [aut, cre], Yesuel Kim [ctb]

rMOST_1.0.1.tar.gz
rMOST_1.0.1.zip(r-4.7-any)rMOST_1.0.1.zip(r-4.6-any)rMOST_1.0.1.zip(r-4.5-any)
rMOST_1.0.1.tgz(r-4.6-any)rMOST_1.0.1.tgz(r-4.5-any)
rMOST_1.0.1.tar.gz(r-4.7-any)rMOST_1.0.1.tar.gz(r-4.6-any)
rMOST_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rMOST/json (API)

# Install 'rMOST' in R:

install.packages('rMOST', repos = c('https://diversity-paretooptimal.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 4 scripts 225 downloads 5 exports 1 dependencies

Last updated from:7c4eb385d9. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	189
source / vignettes	OK	206
linux-release	OK	188
macos-release	OK	198
macos-oldrel	OK	146
windows-devel	OK	142
windows-release	OK	150
windows-oldrel	OK	143
wasm-release	OK	83