Gaussian process regression, classification, and count models with explicit covariance kernels, exact Gaussian inference, Laplace approximations for non-Gaussian likelihoods, sparse approximations, marginal-likelihood optimization, derivatives, posterior sampling, calibration diagnostics, and time-series workflows. The implementation follows the core methodology of Rasmussen and Williams (2006) <doi:10.7551/mitpress/3206.001.0001> and emphasizes numerical stability, transparent assumptions, and reproducible statistical workflows.
| Version: | 1.0.0 |
| Imports: | stats |
| Suggests: | DiceKriging, gplite, kernlab, knitr, nlme, rmarkdown, testthat (≥ 3.0.0), yaml |
| Published: | 2026-10-21 |
| DOI: | 10.32614/CRAN.package.gaussianprocesses (may not be active yet) |
| Author: | Diogo Ribeiro |
| Maintainer: | Diogo Ribeiro <dfr at esmad.ipp.pt> |
| License: | MIT + file LICENSE |
| URL: | https://gaussian-processes-r-12efed.gitlab.io/, https://gitlab.com/DiogoRibeiro7/gaussian-processes-r |
| NeedsCompilation: | no |
| Language: | en-US |
| Citation: | gaussianprocesses citation info |
| Materials: | NEWS |
| CRAN checks: | gaussianprocesses results |
| Package source: | gaussianprocesses_1.0.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Please use the canonical form https://CRAN.R-project.org/package=gaussianprocesses to link to this page.