liGP: Locally Induced Gaussian Process Regression
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, <arXiv:2008.12857>. 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.
||R (≥ 3.4)
||hetGP, laGP, doParallel, foreach
||D. Austin Cole [aut, cre],
Ryan B Christianson [cph],
Robert B. Gramacy [cph]
||D. Austin Cole <austin.cole8 at vt.edu>
||LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
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