GPareto
Gaussian Processes for Pareto Front Estimation and Optimization
Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.9 |
rolling linux/jammy R-4.5 | GPareto_1.1.9.tar.gz |
1.2 MiB |
1.1.9 |
rolling linux/noble R-4.5 | GPareto_1.1.9.tar.gz |
1.2 MiB |
1.1.9 |
rolling source/ R- | GPareto_1.1.9.tar.gz |
1.0 MiB |
1.1.9 |
latest linux/jammy R-4.5 | GPareto_1.1.9.tar.gz |
1.2 MiB |
1.1.9 |
latest linux/noble R-4.5 | GPareto_1.1.9.tar.gz |
1.2 MiB |
1.1.9 |
latest source/ R- | GPareto_1.1.9.tar.gz |
1.0 MiB |
1.1.9 |
2026-04-26 source/ R- | GPareto_1.1.9.tar.gz |
1.0 MiB |
1.1.9 |
2026-04-23 source/ R- | GPareto_1.1.9.tar.gz |
1.0 MiB |
1.1.9 |
2026-04-09 windows/windows R-4.5 | GPareto_1.1.9.zip |
1.5 MiB |
1.1.8 |
2025-04-20 source/ R- | GPareto_1.1.8.tar.gz |
1.0 MiB |