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.
README
# bigGP R package for parallel computation for Gaussian processes
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.9 |
rolling linux/jammy R-4.5 | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
rolling linux/noble R-4.5 | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
rolling source/ R- | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
latest linux/jammy R-4.5 | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
latest linux/noble R-4.5 | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
latest source/ R- | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
2026-04-26 source/ R- | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
2026-04-23 source/ R- | bigGP_0.1.9.tar.gz |
1.0 MiB |
0.1.9 |
2025-04-20 source/ R- | bigGP_0.1.9.tar.gz |
1.0 MiB |