bigGP: Distributed Gaussian Process Calculations
Distributes Gaussian process calculations across nodes
in a distributed memory setting, using Rmpi. The bigGP class
provides high-level methods for maximum likelihood with normal data,
prediction, calculation of uncertainty (i.e., posterior covariance
calculations), and simulation of realizations. In addition, bigGP
provides an API for basic matrix calculations with distributed
covariance matrices, including Cholesky decomposition, back/forwardsolve,
crossproduct, and matrix multiplication.
| Version: |
0.1.9 |
| Depends: |
R (≥ 3.0.0), Rmpi (≥ 0.6-2), methods |
| Suggests: |
rlecuyer, fields |
| OS_type: |
unix |
| Published: |
2025-04-09 |
| DOI: |
10.32614/CRAN.package.bigGP |
| Author: |
Christopher Paciorek [aut, cre],
Benjamin Lipshitz [aut],
Prabhat [ctb],
Cari Kaufman [ctb],
Tina Zhuo [ctb],
Rollin Thomas [ctb] |
| Maintainer: |
Christopher Paciorek <paciorek at stat.berkeley.edu> |
| BugReports: |
https://github.com/paciorek/bigGP/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://doi.org/10.18637/jss.v063.i10 |
| NeedsCompilation: |
yes |
| SystemRequirements: |
OpenMPI or MPICH2 |
| Citation: |
bigGP citation info |
| Materials: |
README, NEWS, INSTALL |
| CRAN checks: |
bigGP results |
Documentation:
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