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Gaussian processes are flexible distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. This package implements two methods for scaling Gaussian process inference in 'Stan'. First, a sparse approximation of the likelihood that is generally applicable and, second, an exact method for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface. References: Hoffmann and Onnela (2022) <doi:10.48550/arXiv.2301.08836>.
Version: | 0.1.0 |
Suggests: | knitr, rmarkdown, cmdstanr |
Published: | 2023-12-19 |
DOI: | 10.32614/CRAN.package.gptoolsStan |
Author: | Till Hoffmann |
Maintainer: | Till Hoffmann <thoffmann at hsph.harvard.edu> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Additional_repositories: | https://mc-stan.org/r-packages/ |
Language: | en-US |
Materials: | README NEWS |
CRAN checks: | gptoolsStan results |
Reference manual: | gptoolsStan.pdf |
Vignettes: |
Getting Started with gptools in R |
Package source: | gptoolsStan_0.1.0.tar.gz |
Windows binaries: | r-devel: gptoolsStan_0.1.0.zip, r-release: gptoolsStan_0.1.0.zip, r-oldrel: gptoolsStan_0.1.0.zip |
macOS binaries: | r-release (arm64): gptoolsStan_0.1.0.tgz, r-oldrel (arm64): gptoolsStan_0.1.0.tgz, r-release (x86_64): gptoolsStan_0.1.0.tgz, r-oldrel (x86_64): gptoolsStan_0.1.0.tgz |
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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.
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