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GEMSS: Generalization Error Minimization in SubSampling for Gaussian Processes

Implements the Generalization Error Minimization in SubSampling (GEMSS) algorithm for sequential subdata selection in large-scale Gaussian process modeling (Chang, Hua, and Wu, 2026) <doi:10.1080/00401706.2026.2670596>. The method selects data points by a criterion consisting of predictive and space-filling parts, enabling efficient surrogate modeling for massive datasets.

Version: 0.1.1
Imports: Rcpp (≥ 1.0.0), hetGP, twinning
LinkingTo: Rcpp, RcppArmadillo
Suggests: ContourFunctions
Published: 2026-05-27
DOI: 10.32614/CRAN.package.GEMSS
Author: Sheng-Zhan Hua [aut, cre]
Maintainer: Sheng-Zhan Hua <szhua at g.ucla.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: GEMSS citation info
Materials: README
CRAN checks: GEMSS results

Documentation:

Reference manual: GEMSS.html , GEMSS.pdf

Downloads:

Package source: GEMSS_0.1.1.tar.gz
Windows binaries: r-devel: GEMSS_0.1.1.zip, r-release: GEMSS_0.1.1.zip, r-oldrel: GEMSS_0.1.1.zip
macOS binaries: r-release (arm64): GEMSS_0.1.1.tgz, r-oldrel (arm64): GEMSS_0.1.1.tgz, r-release (x86_64): GEMSS_0.1.1.tgz, r-oldrel (x86_64): GEMSS_0.1.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GEMSS to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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