bigGP: Distributed Gaussian process calculations
bigGP distributes Gaussian process calculations across nodes
in a distributed memory setting, using Rmpi. The 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: |
0.1-3 |
| Depends: |
R (≥ 3.0.0), Rmpi (≥ 0.6-2), methods |
| Suggests: |
rlecuyer, rsprng, fields |
| OS_type: |
unix |
| Published: |
2014-05-24 |
| 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> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
http://www.stat.berkeley.edu/~paciorek/code/bigGP |
| NeedsCompilation: |
yes |
| SystemRequirements: |
OpenMPI or MPICH2 |
| Materials: |
README NEWS INSTALL |
| CRAN checks: |
bigGP results |
Downloads: