doi:10.1287/mnsc.2023.01796>. You provide an input sample, an output sample, decide the algorithm, and compute the indices.">

gsaot: Compute Global Sensitivity Analysis Indices Using Optimal Transport (original) (raw)

Computing Global Sensitivity Indices from given data using Optimal Transport, as defined in Borgonovo et al (2024) <doi:10.1287/mnsc.2023.01796>. You provide an input sample, an output sample, decide the algorithm, and compute the indices.

Version: 1.1.1
Imports: boot, ggplot2, patchwork (≥ 1.2.0), Rcpp, RcppEigen (≥ 0.3.4.0.0), Rdpack (≥ 2.4), stats, transport (≥ 0.15.0)
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-09-17
DOI: 10.32614/CRAN.package.gsaot
Author: Leonardo Chiani ORCID iD [aut, cre, cph], Emanuele Borgonovo [rev], Elmar Plischke [rev], Massimo Tavoni [rev]
Maintainer: Leonardo Chiani <leonardo.chiani at polimi.it>
BugReports: https://github.com/pietrocipolla/gsaot/issues
License: GPL (≥ 3)
URL: https://github.com/pietrocipolla/gsaot,https://pietrocipolla.github.io/gsaot/
NeedsCompilation: yes
Citation: gsaot citation info
Materials: README, NEWS
CRAN checks: gsaot results

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