Package: GpGp 0.5.1
GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation
Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.
Authors:
GpGp_0.5.1.tar.gz
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GpGp_0.5.1.tgz(r-4.4-x86_64)GpGp_0.5.1.tgz(r-4.4-arm64)GpGp_0.5.1.tgz(r-4.3-x86_64)GpGp_0.5.1.tgz(r-4.3-arm64)
GpGp_0.5.1.tar.gz(r-4.5-noble)GpGp_0.5.1.tar.gz(r-4.4-noble)
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GpGp.pdf |GpGp.html✨
GpGp/json (API)
NEWS
# Install 'GpGp' in R: |
install.packages('GpGp', repos = c('https://joeguinness.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/joeguinness/gpgp/issues
Last updated 22 days agofrom:0e97fa8e4a. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 15 2024 |
R-4.5-win-x86_64 | OK | Oct 15 2024 |
R-4.5-linux-x86_64 | OK | Oct 15 2024 |
R-4.4-win-x86_64 | OK | Oct 15 2024 |
R-4.4-mac-x86_64 | OK | Oct 15 2024 |
R-4.4-mac-aarch64 | OK | Oct 15 2024 |
R-4.3-win-x86_64 | OK | Oct 15 2024 |
R-4.3-mac-x86_64 | OK | Oct 15 2024 |
R-4.3-mac-aarch64 | OK | Oct 15 2024 |
Exports:cond_simcondition_numberd_exponential_anisotropic2Dd_exponential_anisotropic3Dd_exponential_anisotropic3D_altd_exponential_isotropicd_exponential_nonstat_vard_exponential_scaledimd_exponential_spacetimed_exponential_sphered_exponential_sphere_warpd_exponential_spheretimed_exponential_spheretime_warpd_matern_anisotropic2Dd_matern_anisotropic3Dd_matern_anisotropic3D_altd_matern_categoricald_matern_isotropicd_matern_nonstat_vard_matern_scaledimd_matern_spacetimed_matern_spacetime_categoricald_matern_spacetime_categorical_locald_matern_sphered_matern_sphere_warpd_matern_spheretimed_matern_spheretime_warpd_matern15_isotropicd_matern15_scaledimd_matern25_isotropicd_matern25_scaledimd_matern35_isotropicd_matern35_scaledimd_matern45_isotropicd_matern45_scaledimddpen_hiddpen_loddpen_loglodpen_hidpen_lodpen_logloexpitexponential_anisotropic2Dexponential_anisotropic3Dexponential_anisotropic3D_altexponential_isotropicexponential_nonstat_varexponential_scaledimexponential_spacetimeexponential_sphereexponential_sphere_warpexponential_spheretimeexponential_spheretime_warpfast_Gp_simfast_Gp_sim_Linvfind_ordered_nnfind_ordered_nn_brutefisher_scoringfit_modelget_linkfunget_penaltyget_start_parmsgroup_obsintexpitL_multL_t_multLinv_multLinv_t_multmatern_anisotropic2Dmatern_anisotropic3Dmatern_anisotropic3D_altmatern_categoricalmatern_isotropicmatern_nonstat_varmatern_scaledimmatern_spacetimematern_spacetime_categoricalmatern_spacetime_categorical_localmatern_spherematern_sphere_warpmatern_spheretimematern_spheretime_warpmatern15_isotropicmatern15_scaledimmatern25_isotropicmatern25_scaledimmatern35_isotropicmatern35_scaledimmatern45_isotropicmatern45_scaledimorder_coordinateorder_dist_to_pointorder_maxminorder_middleoutpen_hipen_lopen_loglopredictionssph_grad_xyzsummary.GpGp_fittest_likelihood_objectvecchia_grouped_meanzero_loglikvecchia_grouped_profbeta_loglikvecchia_grouped_profbeta_loglik_grad_infovecchia_Linvvecchia_meanzero_loglikvecchia_profbeta_loglikvecchia_profbeta_loglik_grad_info
Dependencies:BHFNNRcppRcppArmadillo