Package: gaussianprocesses
Type: Package
Title: Gaussian Processes from First Principles
Version: 1.0.0.9000
Authors@R: 
    person(
        given = "Diogo",
        family = "Ribeiro",
        role = c("aut", "cre"),
        email = "dfr@esmad.ipp.pt",
        comment = c(
            ORCID = "0009-0001-2022-7072",
            affiliation = "Faculty of Media Arts and Design of the Technical University of Porto"
        )
    )
Description: 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.
License: MIT + file LICENSE
URL: https://gaussian-processes-r-12efed.gitlab.io/,
        https://gitlab.com/DiogoRibeiro7/gaussian-processes-r
Encoding: UTF-8
Language: en-US
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
Imports: stats
Suggests: DiceKriging, gplite, kernlab, knitr, nlme, rmarkdown,
        testthat (>= 3.0.0), yaml
VignetteBuilder: knitr
Config/testthat/edition: 3
Repository: https://diogoribeiro7.r-universe.dev
Date/Publication: 2026-10-22 08:15:07 UTC
RemoteUrl: https://gitlab.com/diogoribeiro7/gaussian-processes-r
RemoteRef: HEAD
RemoteSha: 8770c93ab0d47ef341c31442cad45f565f6403c5
NeedsCompilation: no
Packaged: 2026-10-22 10:33:13 UTC; root
Author: Diogo Ribeiro [aut, cre] (ORCID:
    <https://orcid.org/0009-0001-2022-7072>, affiliation: Faculty of
    Media Arts and Design of the Technical University of Porto)
Maintainer: Diogo Ribeiro <dfr@esmad.ipp.pt>