GPM
3.0.1Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets
Overview
Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.
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Documentation
- Examples that run
- 100%
- Documented parameters
- 88%
- Return-value docs
- 75%
- References docs
- 88%
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Code & Tests
People & History
6 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- archivedRemoved from CRAN2025-12-25issues were not addressed in time
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 3.0.12019-03-21 · diff ↗
- 3.0.02019-01-11 · diff ↗
- 2.0.02018-11-14 · diff ↗
- 1.0.22018-07-23 · diff ↗
- 1.0.12018-06-03 · diff ↗
- RR 3.5.0 released · 2018-04-23
- 1.02018-01-05
Show 1 earlier events
- RR 3.4.0 released · 2017-04-21
Package metadata
- Total releases
- 6
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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