activegp: Gaussian Process Based Design and Analysis for the Active Subspace Method

The active subspace method is a sensitivity analysis technique that finds important linear combinations of input variables for a simulator. This package provides functions allowing estimation of the active subspace without gradient information using Gaussian processes as well as sequential experimental design tools to minimize the amount of data required to do so. Implements Wycoff et al. (JCGS, 2021) <doi:10.48550/arXiv.1907.11572>.

Version: 1.1.1
Depends: R (≥ 3.4.0)
Imports: Rcpp (≥ 0.12.18), hetGP (≥ 1.1.1), lhs, numDeriv, methods, MASS, RcppProgress
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
Suggests: testthat
Published: 2024-05-25
DOI: 10.32614/CRAN.package.activegp
Author: Nathan Wycoff, Mickael Binois
Maintainer: Nathan Wycoff <nathan.wycoff at>
License: BSD_3_clause + file LICENSE
NeedsCompilation: yes
Materials: NEWS
CRAN checks: activegp results


Reference manual: activegp.pdf


Package source: activegp_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): activegp_1.1.1.tgz, r-oldrel (arm64): activegp_1.1.1.tgz, r-release (x86_64): activegp_1.1.1.tgz, r-oldrel (x86_64): activegp_1.1.1.tgz
Old sources: activegp archive


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