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 (may not be active yet)
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: not available, r-release: not available, r-oldrel: not available
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:

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