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.
- Version0.1.8
- R version≥ 3.0.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- OSunix
- bigGP citation info
- Last release04/25/2023
Documentation
Team
Christopher Paciorek
Benjamin Lipshitz
Show author detailsRolesAuthorPrabhat
Show author detailsRolesContributorCari Kaufman
Show author detailsRolesContributorTina Zhuo
Show author detailsRolesContributorRollin Thomas
Show author detailsRolesContributor
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- Depends2 packages
- Suggests2 packages