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GPM  

Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets
View on CRAN: Click here


Download and install GPM package within the R console
Install from CRAN:
install.packages("GPM")

Install from Github:
library("remotes")
install_github("cran/GPM")

Install by package version:
library("remotes")
install_version("GPM", "3.0.1")



Attach the package and use:
library("GPM")
Maintained by
Ramin Bostanabad
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-06-03
Latest Update: 2019-03-21
Description:
Provides a general and efficient tool for fitting a response surface to a dataset via Gaussian processes. The dataset can have multiple responses and be noisy (with stationary variance). The fitted GP model can predict the gradient as well. The package is based on the work of Bostanabad, R., Kearney, T., Tao, S. Y., Apley, D. W. & Chen, W. (2018) Leveraging the nugget parameter for efficient Gaussian process modeling. International Journal for Numerical Methods in Engineering, 114, 501-516.
How to cite:
Ramin Bostanabad (2018). GPM: Gaussian Process Modeling of Multi-Response and Possibly Noisy Datasets. R package version 3.0.1, https://cran.r-project.org/web/packages/GPM
Previous versions and publish date:
1.0.1 (2018-06-03 19:43), 1.0.2 (2018-07-23 20:30), 1.0 (2018-01-05 13:10), 2.0.0 (2018-11-14 16:00), 3.0.0 (2019-01-11 18:20)
Other packages that cited GPM R package
View GPM citation profile
Other R packages that GPM depends, imports, suggests or enhances
Functions, R codes and Examples using the GPM R package
Some associated functions: Auxil . CorrMat . Draw . Fit . MatrixAlgebra . NLogL . NLogL_G . Predict . 
Some associated R codes: Auxil.R . Draw.R . Fit.R . NLogL.R . NLogL_G.R . Predict.R . RcppExports.R .  Full GPM package functions and examples
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