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README.md
In leapgp: Localized Ensemble of Approximate Gaussian Processes
leapgp
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Description
leapgp is an R package designed for fast sequential emulation of
computer models. Sequential emulation is common in applications
requiring MCMC. The leapgp package is based on Rumsey et
al. (2023)
and can be viewed as a global model extension of the laGP method of
Apley and Gramacy 2015.
To install the package, use
# install.packages("devtools")
devtools::install_github("knrumsey/leapgp")
The method is described in Rumsey et
al. (2023)
and explicit code examples are given in the vignette associated with
this package.
References
Rumsey, Kellin N., Gabriel Huerta, and J. Derek Tucker. “A localized
ensemble of approximate Gaussian processes for fast sequential
emulation.” Stat (2023): e576.
Gramacy, Robert B., and Daniel W. Apley. “Local Gaussian process
approximation for large computer experiments.” Journal of Computational
and Graphical Statistics 24.2 (2015): 561-578.
Try the leapgp package in your browser
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leapgp documentation built on Sept. 11, 2024, 7:25 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
leapgp
This logo was designed by Imagine AI Art Studio
Description
leapgp is an R package designed for fast sequential emulation of
computer models. Sequential emulation is common in applications
requiring MCMC. The leapgp package is based on Rumsey et
al. (2023)
and can be viewed as a global model extension of the laGP method of
Apley and Gramacy 2015.
To install the package, use
# install.packages("devtools")
devtools::install_github("knrumsey/leapgp")
The method is described in Rumsey et al. (2023) and explicit code examples are given in the vignette associated with this package.
References
Rumsey, Kellin N., Gabriel Huerta, and J. Derek Tucker. “A localized ensemble of approximate Gaussian processes for fast sequential emulation.” Stat (2023): e576.
Gramacy, Robert B., and Daniel W. Apley. “Local Gaussian process approximation for large computer experiments.” Journal of Computational and Graphical Statistics 24.2 (2015): 561-578.
Try the leapgp package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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