LVGP: Latent Variable Gaussian Process Modeling with Qualitative and
Quantitative Input Variables
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.
| Version: |
2.1.6 |
| Depends: |
R (≥ 3.4.0), stats (≥ 3.2.5), parallel (≥ 3.2.5) |
| Imports: |
lhs (≥ 0.14), randtoolbox (≥ 1.17) |
| Published: |
2026-07-21 |
| DOI: |
10.32614/CRAN.package.LVGP |
| Author: |
Siyu Tao [aut, cre],
Yichi Zhang [aut],
Daniel W. Apley [aut],
Wei Chen [aut] |
| Maintainer: |
Siyu Tao <siyutao2020 at u.northwestern.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
LVGP results |
Documentation:
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