# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "gaussianprocesses" in publications use:' type: software license: MIT title: 'gaussianprocesses: Gaussian Processes from First Principles' version: 1.0.0.9000 identifiers: - type: doi value: 10.32614/CRAN.package.gaussianprocesses abstract: 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) and emphasizes numerical stability, transparent assumptions, and reproducible statistical workflows. authors: - family-names: Ribeiro given-names: Diogo email: dfr@esmad.ipp.pt orcid: https://orcid.org/0009-0001-2022-7072 affiliation: Faculty of Media Arts and Design of the Technical University of Porto preferred-citation: type: manual title: 'gaussianprocesses: Gaussian Processes from First Principles' authors: - family-names: Ribeiro given-names: Diogo email: dfr@esmad.ipp.pt orcid: https://orcid.org/0009-0001-2022-7072 affiliation: Faculty of Media Arts and Design of the Technical University of Porto year: '2026' notes: R package version 1.0.0.9000 url: https://gitlab.com/DiogoRibeiro7/gaussian-processes-r repository: https://diogoribeiro7.r-universe.dev repository-code: https://gitlab.com/DiogoRibeiro7/gaussian-processes-r commit: 8770c93ab0d47ef341c31442cad45f565f6403c5 url: https://gaussian-processes-r-12efed.gitlab.io/ date-released: '2026-10-22' contact: - family-names: Ribeiro given-names: Diogo email: dfr@esmad.ipp.pt orcid: https://orcid.org/0009-0001-2022-7072 affiliation: Faculty of Media Arts and Design of the Technical University of Porto