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LVGP
View on CRAN: Click
here
Download and install LVGP package within the R console
Install from CRAN:
install.packages("LVGP")
Install from Github:
library("remotes")
install_github("cran/LVGP") Install by package version:
library("remotes")
install_version("LVGP", "2.1.5") Attach the package and use:
library("LVGP")
Maintained by
Siyu Tao
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-07-31
Latest Update: 2019-01-11
Description:
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) . The package is developed in IDEAL of Northwestern University.
How to cite:
Siyu Tao (2018). LVGP: Latent Variable Gaussian Process Modeling with Qualitative and Quantitative Input Variables. R package version 2.1.5, https://cran.r-project.org/web/packages/LVGP. Accessed 05 Jun. 2026.
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Complete documentation for LVGP
Functions, R codes and Examples using
the LVGP R package
Some associated functions: LVGP_fit . LVGP_plot . LVGP_predict . corr_mat . math_example . neg_log_l . to_latent .
Some associated R codes: LVGP_fit.R . LVGP_plot.R . LVGP_predict.R . corr_mat.R . example-data.R . neg_log_l.R . to_latent.R . Full LVGP package functions and examples
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