{ "_id": "6a1031f7acfb0bcc41c974df", "Package": "GpGp", "Type": "Package", "Title": "Fast Gaussian Process Computation Using Vecchia's Approximation", "Version": "1.0.0", "Date": "2025-12-17", "Authors@R": "c(\nperson(\"Joseph\", \"Guinness\", email = \"joeguinness@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Matthias\", \"Katzfuss\", email = \"katzfuss@gmail.com\", role = \"aut\" ),\nperson(\"Youssef\", \"Fahmy\", email = \"yf297@cornell.edu\", role = \"aut\" ) )", "Maintainer": "Joseph Guinness ", "Description": "Functions for fitting and doing predictions with Gaussian\nprocess models using Vecchia's (1988) approximation. Package\nalso includes functions for reordering input locations, finding\nordered nearest neighbors (with help from 'FNN' package),\ngrouping operations, and conditional simulations. Covariance\nfunctions for spatial and spatial-temporal data on Euclidean\ndomains and spheres are provided. The original approximation is\ndue to Vecchia (1988) ,\nand the reordering and grouping methods are from Guinness\n(2018) . Model fitting\nemploys a Fisher scoring algorithm described in Guinness (2019)\n.", "License": "MIT + file LICENSE", "RoxygenNote": "7.3.3", "LazyData": "true", "Repository": "https://joeguinness.r-universe.dev", "Date/Publication": "2025-12-17 20:31:27 UTC", "RemoteUrl": "https://github.com/joeguinness/gpgp", "RemoteRef": "HEAD", "RemoteSha": "95445c66f273a957400fa6261888dd1637ee5c5f", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-22 10:31:21 UTC", "User": "root" }, "Author": "Joseph Guinness [aut, cre],\nMatthias Katzfuss [aut],\nYoussef Fahmy [aut]", "MD5sum": "176dd582fe4c75f09c10f1736f9b921c", "_user": "joeguinness", "_type": "src", "_file": "GpGp_1.0.0.tar.gz", "_fileid": "42765b39fe9a1027157d497f8367ecfb69285a89143f35412c0d41c4eb8cb048", "_filesize": 1688345, "_sha256": "42765b39fe9a1027157d497f8367ecfb69285a89143f35412c0d41c4eb8cb048", "_created": "2026-05-22T10:31:21.000Z", "_published": "2026-05-22T10:37:43.272Z", "_distro": "noble", "_jobs": [ { "job": 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"d_exponential_anisotropic3D", "exponential_anisotropic3D" ] }, { "page": "exponential_anisotropic3D_alt", "title": "Geometrically anisotropic exponential covariance function (three dimensions, alternate parameterization)", "topics": [ "d_exponential_anisotropic3D_alt", "exponential_anisotropic3D_alt" ] }, { "page": "exponential_isotropic", "title": "Isotropic exponential covariance function", "topics": [ "d_exponential_isotropic", "d_matern15_isotropic", "d_matern25_isotropic", "exponential_isotropic" ] }, { "page": "exponential_nonstat_var", "title": "Isotropic exponential covariance function, nonstationary variances", "topics": [ "d_exponential_nonstat_var", "exponential_nonstat_var" ] }, { "page": "exponential_scaledim", "title": "Exponential covariance function, different range parameter for each dimension", "topics": [ "d_exponential_scaledim", "exponential_scaledim" ] }, { "page": "exponential_spacetime", "title": "Spatial-Temporal exponential covariance function", "topics": [ "d_exponential_spacetime", "exponential_spacetime" ] }, { "page": "exponential_sphere", "title": "Isotropic exponential covariance function on sphere", "topics": [ "d_exponential_sphere", "exponential_sphere" ] }, { "page": "exponential_sphere_warp", "title": "Deformed exponential covariance function on sphere", "topics": [ "d_exponential_sphere_warp", "exponential_sphere_warp" ] }, { "page": "exponential_spheretime", "title": "Exponential covariance function on sphere x time", "topics": [ "d_exponential_spheretime", "exponential_spheretime" ] }, { "page": "exponential_spheretime_warp", "title": "Deformed exponential covariance function on sphere", "topics": [ "d_exponential_spheretime_warp", "exponential_spheretime_warp" ] }, { "page": "fast_Gp_sim", "title": "Approximate GP simulation", "topics": [ "fast_Gp_sim" ] }, { "page": "fast_Gp_sim_Linv", "title": "Approximate GP simulation with specified Linverse", "topics": [ "fast_Gp_sim_Linv" ] }, { "page": "find_ordered_nn", "title": "Find ordered nearest neighbors.", "topics": [ "find_ordered_nn" ] }, { "page": "find_ordered_nn_brute", "title": "Naive brute force nearest neighbor finder", "topics": [ "find_ordered_nn_brute" ] }, { "page": "fisher_scoring", "title": "Fisher scoring algorithm", "topics": [ "fisher_scoring" ] }, { "page": "fit_model", "title": "Estimate mean and covariance parameters", "topics": [ "fit_model" ] }, { "page": "get_linkfun", "title": "get link function, whether locations are lonlat and space time", "topics": [ "get_linkfun" ] }, { "page": "get_penalty", "title": "get penalty function", "topics": [ "get_penalty" ] }, { "page": "get_start_parms", "title": "get default starting values of covariance parameters", "topics": [ "get_start_parms" ] }, { "page": "GpGp", "title": "GpGp: Fast Gaussian Process Computing.", "topics": [ "GpGp-package", "GpGp" ] }, { "page": "group_obs", "title": "Automatic grouping (partitioning) of locations", "topics": [ "group_obs" ] }, { "page": "jason3", "title": "Windspeed measurements from Jason-3 Satellite", "topics": [ "jason3" ] }, { "page": "L_mult", "title": "Multiply approximate Cholesky by a vector", "topics": [ "L_mult" ] }, { "page": "L_t_mult", "title": "Multiply transpose of approximate Cholesky by a vector", "topics": [ "L_t_mult" ] }, { "page": "Linv_mult", "title": "Multiply approximate inverse Cholesky by a vector", "topics": [ "Linv_mult" ] }, { "page": "Linv_t_mult", "title": "Multiply transpose of approximate inverse Cholesky by a vector", "topics": [ "Linv_t_mult" ] }, { "page": "matern_anisotropic2D", "title": "Geometrically anisotropic Matern covariance function (two dimensions)", "topics": [ "d_matern_anisotropic2D", "matern_anisotropic2D" ] }, { "page": "matern_anisotropic3D", "title": "Geometrically anisotropic Matern covariance function (three dimensions)", "topics": [ "d_matern_anisotropic3D", "d_matern_anisotropic3D_alt", "matern_anisotropic3D" ] }, { "page": "matern_anisotropic3D_alt", "title": "Geometrically anisotropic Matern covariance function (three dimensions, alternate parameterization)", "topics": [ "matern_anisotropic3D_alt" ] }, { "page": "matern_categorical", "title": "Isotropic Matern covariance function with random effects for categories", "topics": [ "d_matern_categorical", "matern_categorical" ] }, { "page": "matern_isotropic", "title": "Isotropic Matern covariance function", "topics": [ "d_matern_isotropic", "matern_isotropic" ] }, { "page": "matern_nonstat_var", "title": "Isotropic Matern covariance function, nonstationary variances", "topics": [ "d_matern_nonstat_var", "matern_nonstat_var" ] }, { "page": "matern_scaledim", "title": "Matern covariance function, different range parameter for each dimension", "topics": [ "d_matern_scaledim", "matern_scaledim" ] }, { "page": "matern_spacetime", "title": "Spatial-Temporal Matern covariance function", "topics": [ "d_matern_spacetime", "matern_spacetime" ] }, { "page": "matern_spacetime_categorical", "title": "Space-Time Matern covariance function with random effects for categories", "topics": [ "d_matern_spacetime_categorical", "matern_spacetime_categorical" ] }, { "page": "matern_spacetime_categorical_local", "title": "Space-Time Matern covariance function with local random effects for categories", "topics": [ "d_matern_spacetime_categorical_local", "matern_spacetime_categorical_local" ] }, { "page": "matern_sphere", "title": "Isotropic Matern covariance function on sphere", "topics": [ "d_matern_sphere", "matern_sphere" ] }, { "page": "matern_sphere_warp", "title": "Deformed Matern covariance function on sphere", "topics": [ "d_matern_sphere_warp", "matern_sphere_warp" ] }, { "page": "matern_spheretime", "title": "Matern covariance function on sphere x time", "topics": [ "d_matern_spheretime", "matern_spheretime" ] }, { "page": "matern_spheretime_warp", "title": "Deformed Matern covariance function on sphere", "topics": [ "d_matern_spheretime_warp", "matern_spheretime_warp" ] }, { "page": "matern15_isotropic", "title": "Isotropic Matern covariance function, smoothness = 1.5", "topics": [ "matern15_isotropic" ] }, { "page": "matern15_scaledim", "title": "Matern covariance function, smoothess = 1.5, different range parameter for each dimension", "topics": [ "d_matern15_scaledim", "matern15_scaledim" ] }, { "page": "matern25_isotropic", "title": "Isotropic Matern covariance function, smoothness = 2.5", "topics": [ "matern25_isotropic" ] }, { "page": "matern25_scaledim", "title": "Matern covariance function, smoothess = 2.5, different range parameter for each dimension", "topics": [ "d_matern25_scaledim", "matern25_scaledim" ] }, { "page": "matern35_isotropic", "title": "Isotropic Matern covariance function, smoothness = 3.5", "topics": [ "d_matern35_isotropic", "d_matern45_isotropic", "matern35_isotropic" ] }, { "page": "matern35_scaledim", "title": "Matern covariance function, smoothess = 3.5, different range parameter for each dimension", "topics": [ "d_matern35_scaledim", "d_matern45_scaledim", "matern35_scaledim" ] }, { "page": "matern45_isotropic", "title": "Isotropic Matern covariance function, smoothness = 4.5", "topics": [ "matern45_isotropic" ] }, { "page": "matern45_scaledim", "title": "Matern covariance function, smoothess = 3.5, different range parameter for each dimension", "topics": [ "matern45_scaledim" ] }, { "page": "order_coordinate", "title": "Sorted coordinate ordering", "topics": [ "order_coordinate" ] }, { "page": "order_dist_to_point", "title": "Distance to specified point ordering", "topics": [ "order_dist_to_point" ] }, { "page": "order_maxmin", "title": "Maximum minimum distance ordering", "topics": [ "order_maxmin" ] }, { "page": "order_middleout", "title": "Middle-out ordering", "topics": [ "order_middleout" ] }, { "page": "pen_hi", "title": "penalize large values of parameter: penalty, 1st deriative, 2nd derivative", "topics": [ "ddpen_hi", "dpen_hi", "pen_hi" ] }, { "page": "pen_lo", "title": "penalize small values of parameter: penalty, 1st deriative, 2nd derivative", "topics": [ "ddpen_lo", "dpen_lo", "pen_lo" ] }, { "page": "pen_loglo", "title": "penalize small values of log parameter: penalty, 1st deriative, 2nd derivative", "topics": [ "ddpen_loglo", "dpen_loglo", "pen_loglo" ] }, { "page": "predictions", "title": "Compute Gaussian process predictions using Vecchia's approximations", "topics": [ "predictions" ] }, { "page": "sph_grad_xyz", "title": "compute gradient of spherical harmonics functions", "topics": [ "sph_grad_xyz" ] }, { "page": "summary.GpGp_fit", "title": "Print summary of GpGp fit", "topics": [ "summary.GpGp_fit" ] }, { "page": "test_likelihood_object", "title": "test likelihood object for NA or Inf values", "topics": [ "test_likelihood_object" ] }, { "page": "vecchia_grouped_meanzero_loglik", "title": "Grouped Vecchia approximation to the Gaussian loglikelihood, zero mean", "topics": [ "vecchia_grouped_meanzero_loglik" ] }, { "page": "vecchia_grouped_profbeta_loglik", "title": "Grouped Vecchia approximation, profiled regression coefficients", "topics": [ "vecchia_grouped_profbeta_loglik" ] }, { "page": "vecchia_grouped_profbeta_loglik_grad_info", "title": "Grouped Vecchia loglikelihood, gradient, Fisher information", "topics": [ "vecchia_grouped_profbeta_loglik_grad_info" ] }, { "page": "vecchia_Linv", "title": "Entries of inverse Cholesky approximation", "topics": [ "vecchia_Linv" ] }, { "page": "vecchia_meanzero_loglik", "title": "Vecchia's approximation to the Gaussian loglikelihood, zero mean", "topics": [ "vecchia_meanzero_loglik" ] }, { "page": "vecchia_profbeta_loglik", "title": "Vecchia's approximation to the Gaussian loglikelihood, with profiled regression coefficients.", "topics": [ "vecchia_profbeta_loglik" ] }, { "page": "vecchia_profbeta_loglik_grad_info", "title": "Vecchia's loglikelihood, gradient, and Fisher information", "topics": [ "vecchia_profbeta_loglik_grad_info" ] } ], "_readme": 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