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.

Version: 1.0.1
Depends: R (≥ 3.4)
Imports: hetGP, laGP, doParallel, foreach
Suggests: lhs
Published: 2021-07-17
Author: D. Austin Cole [aut, cre], Ryan B Christianson [cph], Robert B. Gramacy [cph]
Maintainer: D. Austin Cole <austin.cole8 at vt.edu>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: liGP results

Documentation:

Reference manual: liGP.pdf

Downloads:

Package source: liGP_1.0.1.tar.gz
Windows binaries: r-devel: liGP_1.0.1.zip, r-devel-UCRT: liGP_1.0.1.zip, r-release: liGP_1.0.1.zip, r-oldrel: liGP_1.0.1.zip
macOS binaries: r-release (arm64): liGP_1.0.1.tgz, r-release (x86_64): liGP_1.0.1.tgz, r-oldrel: liGP_1.0.1.tgz
Old sources: liGP archive

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