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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "LVGP" in publications use:'
type: software
license: GPL-2.0-only
title: 'LVGP: Latent Variable Gaussian Process Modeling with Qualitative and Quantitative
  Input Variables'
version: 2.1.5
doi: 10.32614/CRAN.package.LVGP
abstract: 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) <arXiv:1806.07504>. The package is
  developed in IDEAL of Northwestern University.
authors:
- family-names: Tao
  given-names: Siyu
  email: siyutao2020@u.northwestern.edu
- family-names: Zhang
  given-names: Yichi
- family-names: Apley
  given-names: Daniel W.
- family-names: Chen
  given-names: Wei
repository: https://siyutao2020.r-universe.dev
commit: 1d8b6193b2f866b3be28eac0c6b98154abb6f7fc
date-released: '2019-01-11'
contact:
- family-names: Tao
  given-names: Siyu
  email: siyutao2020@u.northwestern.edu