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 (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 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) <arXiv:1806.07504>.

Version: 2.1.3
Depends: R (≥ 3.5.0), stats (≥ 3.2.5), parallel (≥ 3.2.5)
Imports: lhs (≥ 0.14), randtoolbox (≥ 1.17), lattice (≥ 0.20-34)
Published: 2018-07-31
Author: Siyu Tao, Yichi Zhang
Maintainer: Siyu Tao <siyutao2020 at>
License: GPL-2
NeedsCompilation: no
CRAN checks: LVGP results


Reference manual: LVGP.pdf
Package source: LVGP_2.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
OS X binaries: r-release: LVGP_2.1.3.tgz, r-oldrel: not available


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