https://cran.r-project.org/package=gaussianprocesses

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) &lt;<a href="https://doi.org/10.7551%2Fmitpress%2F3206.001.0001" target="_top">doi:10.7551/mitpress/3206.001.0001</a>&gt; and emphasizes numerical stability, transparent assumptions, and reproducible statistical workflows.

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Traditional explicit 3D representations, such as point clouds and meshes, demand significant storage to capture fine geometric details and require complex indexing systems for surface lookups, making ...