Package: GEMSS 0.1.1

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.

Authors:Sheng-Zhan Hua [aut, cre]

GEMSS_0.1.1.tar.gz
GEMSS_0.1.1.zip(r-4.7)GEMSS_0.1.1.zip(r-4.6)GEMSS_0.1.1.zip(r-4.5)
GEMSS_0.1.1.tgz(r-4.6-x86_64)GEMSS_0.1.1.tgz(r-4.6-arm64)GEMSS_0.1.1.tgz(r-4.5-x86_64)GEMSS_0.1.1.tgz(r-4.5-arm64)
GEMSS_0.1.1.tar.gz(r-4.7-arm64)GEMSS_0.1.1.tar.gz(r-4.7-x86_64)GEMSS_0.1.1.tar.gz(r-4.6-arm64)GEMSS_0.1.1.tar.gz(r-4.6-x86_64)
GEMSS_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GEMSS/json (API)

# Install 'GEMSS' in R:
install.packages('GEMSS', repos = c('https://szhua-stat.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

1.70 score 4 exports 8 dependencies

Last updated from:483cb60f12. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK128
linux-devel-x86_64OK131
source / vignettesOK180
linux-release-arm64OK122
linux-release-x86_64OK129
macos-release-arm64OK141
macos-release-x86_64OK236
macos-oldrel-arm64OK202
macos-oldrel-x86_64OK253
windows-develOK130
windows-releaseOK125
windows-oldrelOK177
wasm-releaseOK113

Exports:compute_kernelgemss_removegemss_selectgp_predict

Dependencies:DiceDesignhetGPMASSmcoquadprogRcppRcppArmadillotwinning