Latent Variable Gaussian Process Modeling with Qualitative and Quantitative Input Variables
Overview
Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D). The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM". The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors" by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) doi:10.48550/arXiv.1806.07504. The package is developed in IDEAL of Northwestern University.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-27
- NOTE14 NOTE2026-03-10
Documentation
- Help pages
- 7
- Examples that run
- 29%2 run in a plain check · 5 hold no code
- Exported functions with examples
- 100%
- Documented parameters
- 82%
- Return-value docs
- 83%
- Help pages citing references
- 100%
Downloads
Dependencies
Installing LVGP adds 4 packages (4 with compiled code, 3.5 MB of source tarballs). Heaviest: Rcpp 1.8 MB, randtoolbox 1.4 MB, lhs 238 KB.
Code & Tests
Datasets
People & History
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- 2.1.6Latest
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- 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
- 2.1.52019-01-11 · diff ↗
- 2.1.42018-11-14 · diff ↗
- 2.1.32018-07-31
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2018-07-31
- Total releases
- 4 / 8 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.4.0
- Bundled data
- 2.1 KB / 1 file
- Download size
- 15 KB
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