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.

Getting started

Package details

AuthorChristopher Paciorek [aut, cre], Benjamin Lipshitz [aut], Prabhat [ctb], Cari Kaufman [ctb], Tina Zhuo [ctb], Rollin Thomas [ctb]
MaintainerChristopher Paciorek <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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bigGP documentation built on May 2, 2019, 9:29 a.m.