Generalization Error Minimization in SubSampling for Gaussian Processes
Overview
Implements the 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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-17
- WARNING4 OK, 3 WARNING2026-05-28
Documentation
- Help pages
- 4
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 50%
- Documented parameters
- 85%
- Return-value docs
- 100%
- Help pages citing references
- 50%
Downloads
Dependencies
Installing GEMSS adds 7 packages (7 with compiled code, 4.4 MB of source tarballs). Heaviest: Rcpp 1.8 MB, hetGP 1.4 MB, RcppArmadillo 1.0 MB.
Code & Tests
People & History
Pick two releases to compare their code metrics.
- 0.1.2Latest
- 0.1.1first seen 2026-05-27
- RR 4.6.0 released · 2026-04-24
Package metadata
- First seen
- 2026-05-27
- Total releases
- 2 / 1 yrs
- License
- GPL (>= 3) OSI
- Download size
- 17 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("GEMSS")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1080/00401706.2026.2670596.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.