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: 0.1-2
Depends: R (≥ 3.0.0), Rmpi (≥ 0.6-2), methods
Suggests: rlecuyer, rsprng, fields
OS_type: unix
Published: 2013-06-04
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 INSTALL
CRAN checks: bigGP results

Downloads:

Reference manual: bigGP.pdf
Package source: bigGP_0.1-2.tar.gz
MacOS X binary: bigGP_0.1-2.tgz
Windows binary: not available, see ReadMe.