DynamicGP: Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments

Fits localized GP model for dynamic computer experiments via singular value decomposition of the response matrix Y for large N (the number of observations) using the algorithm proposed by Zhang et al. (2018) <arXiv:1611.09488>. The current version only supports 64-bit version of R.

Version: 1.0-2
Depends: R (≥ 2.14)
Imports: lhs, laGP, parallel
Published: 2018-04-18
Author: Ru Zhang [aut, cre], Chunfang Devon Lin [aut], Pritam Ranjan [aut], Robert B Gramacy [ctb], Nicolas Devillard [ctb], Jorge Nocedal [ctb], Jose Luis Morales [ctb], Ciyou Zhu [ctb], Richard Byrd [ctb], Peihuang Lu-Chen [ctb], University of Chicago [cph], University of California [cph]
Maintainer: Ru Zhang <heavenmarshal at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: see file COPYRIGHTS
NeedsCompilation: yes
CRAN checks: DynamicGP results


Reference manual: DynamicGP.pdf
Package source: DynamicGP_1.0-2.tar.gz
Windows binaries: r-devel: DynamicGP_1.0-2.zip, r-release: DynamicGP_1.0-2.zip, r-oldrel: DynamicGP_1.0-2.zip
OS X binaries: r-release: DynamicGP_1.0-2.tgz, r-oldrel: DynamicGP_1.0-2.tgz
Old sources: DynamicGP archive


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