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liGP  

Locally Induced Gaussian Process Regression
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("liGP", "1.0.1")



Attach the package and use:
library("liGP")
Maintained by
D. Austin Cole
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-07-01
Latest Update: 2023-04-18
Description:
Performs locally induced approximate GP regression for large computer experiments and spatial datasets following Cole D.A., Christianson, R., Gramacy, R.B. (2021) Statistics and Computing, 31(3), 1-21, . The approximation is based on small local designs combined with a set of inducing points (latent design points) for predictions at particular inputs. Parallelization is supported for generating predictions over an immense out-of-sample testing set. Local optimization of the inducing points design is provided based on variance-based criteria. Inducing point template schemes, including scaling of space-filling designs, are also provided.
How to cite:
D. Austin Cole (2021). liGP: Locally Induced Gaussian Process Regression. R package version 1.0.1, https://cran.r-project.org/web/packages/liGP. Accessed 03 Dec. 2024.
Previous versions and publish date:
1.0.0 (2021-07-01 09:50), 1.0.1 (2021-07-17 08:00)
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