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Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers.
| Version: | 1.3.3 |
| Depends: | R (≥ 1.9.0) |
| Imports: | graphics, stats, grDevices |
| Published: | 2015-07-01 |
| DOI: | 10.32614/CRAN.package.spectralGP |
| Author: | Chris Paciorek |
| Maintainer: | Chris Paciorek <paciorek at alumni.cmu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://www.jstatsoft.org/v19/a2 |
| NeedsCompilation: | no |
| Citation: | spectralGP citation info |
| Materials: | NEWS |
| CRAN checks: | spectralGP results |
| Reference manual: | spectralGP.pdf |
| Package source: | spectralGP_1.3.3.tar.gz |
| Windows binaries: | r-devel: spectralGP_1.3.3.zip, r-release: spectralGP_1.3.3.zip, r-oldrel: spectralGP_1.3.3.zip |
| macOS binaries: | r-release (arm64): spectralGP_1.3.3.tgz, r-oldrel (arm64): spectralGP_1.3.3.tgz, r-release (x86_64): spectralGP_1.3.3.tgz, r-oldrel (x86_64): spectralGP_1.3.3.tgz |
| Old sources: | spectralGP archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.