Package: gsaot 1.2.0

gsaot: Compute Global Sensitivity Analysis Indices Using Optimal Transport

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.

Authors:Leonardo Chiani [aut, cre, cph], Emanuele Borgonovo [rev], Elmar Plischke [rev], Massimo Tavoni [rev]

gsaot_1.2.0.tar.gz
gsaot_1.2.0.zip(r-4.7-x86_64)gsaot_1.2.0.zip(r-4.7-arm64)gsaot_1.2.0.zip(r-4.6-x86_64)gsaot_1.2.0.zip(r-4.6-arm64)gsaot_1.2.0.zip(r-4.5-x86_64)
gsaot_1.2.0.tgz(r-4.6-x86_64)gsaot_1.2.0.tgz(r-4.6-arm64)gsaot_1.2.0.tgz(r-4.5-x86_64)gsaot_1.2.0.tgz(r-4.5-arm64)
gsaot_1.2.0.tar.gz(r-4.7-arm64)gsaot_1.2.0.tar.gz(r-4.7-x86_64)gsaot_1.2.0.tar.gz(r-4.6-arm64)gsaot_1.2.0.tar.gz(r-4.6-x86_64)
gsaot_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gsaot/json (API)

# Install 'gsaot' in R:

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

Bug tracker:https://github.com/pietrocipolla/gsaot/issues

Pkgdown/docs site:https://pietrocipolla.github.io

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

4.30 score 1 stars 9 scripts 425 downloads 12 exports 25 dependencies

Last updated from:d0e0b78cd4. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	139
linux-devel-x86_64	OK	160
source / vignettes	OK	187
linux-release-arm64	OK	142
linux-release-x86_64	OK	162
macos-release-arm64	OK	167
macos-release-x86_64	OK	343
macos-oldrel-arm64	OK	145
macos-oldrel-x86_64	OK	519
windows-devel-arm64	OK	391
windows-devel-x86_64	OK	193
windows-release-arm64	OK	399
windows-release-x86_64	OK	110
windows-oldrel-x86_64	OK	143
wasm-release	OK	116

Exports:entropic_bound gaussian_fun irrelevance_threshold ishi_homma_fun ot_indices ot_indices_1d ot_indices_smap ot_indices_wb plot_comparison plot_separations residual_gap sobol_fun

Dependencies:boot cli cpp11 data.table farver ggplot2 glue gtable isoband labeling lifecycle patchwork R6 rbibutils RColorBrewer Rcpp RcppEigen Rdpack rlang S7 scales transport vctrs viridisLite withr

Global Sensitivity Analysis of a simple Gaussian model

Introduction | Sensitivity analysis

Rendered fromgaussian-model-sensitivity.Rmdusingknitr::rmarkdown

Last update: 2025-09-17
Started: 2024-04-05

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compute confidence intervals for sensitivity indices	confint.gsaot_indices
Entropic lower bounds for entropic optimal transport sensitivity indices	entropic_bound
Multivariate Gaussian linear model evaluation	gaussian_fun
Irrelevance threshold for optimal transport sensitivity indices	irrelevance_threshold
Ishigami-Homma function evaluation	ishi_homma_fun
Estimate optimal transport sensitivity indices for generic outputs	ot_indices
Estimate optimal transport indices on one dimensional outputs	ot_indices_1d
Estimate sensitivity maps using optimal transport indices	ot_indices_smap
Estimate Wasserstein-Bures approximation of the optimal transport solution	ot_indices_wb
Compare sensitivity indices across methods	plot_comparison
Plot optimal transport local separations	plot_separations
Plot optimal transport sensitivity indices	plot.gsaot_indices
Print optimal transport sensitivity indices information	print.gsaot_indices
Residual gap for optimal transport sensitivity indices based on squared Euclidean ground cost	residual_gap
Sobol G function evaluation	sobol_fun
Summary method for 'gsaot_indices' objects	summary.gsaot_indices