{ "_id": "6a104a55acfb0bcc41c9fa8e", "Package": "BayesNSGP", "Title": "Bayesian Analysis of Non-Stationary Gaussian Process Models", "Description": "Enables off-the-shelf functionality for fully Bayesian,\nnonstationary Gaussian process modeling. The approach to\nnonstationary modeling involves a closed-form,\nconvolution-based covariance function with spatially-varying\nparameters; these parameter processes can be specified either\ndeterministically (using covariates or basis functions) or\nstochastically (using approximate Gaussian processes).\nStationary Gaussian processes are a special case of our\nmethodology, and we furthermore implement approximate Gaussian\nprocess inference to account for very large spatial data sets\n(Finley, et al (2017) ).\nBayesian inference is carried out using Markov chain Monte\nCarlo methods via the \"nimble\" package, and posterior\nprediction for the Gaussian process at unobserved locations is\nprovided as a post-processing step.", "Version": "0.2.0", "Date": "2025-12-11", "Maintainer": "Daniel Turek ", "Authors@R": "c(person(\"Daniel\", \"Turek\", role = c(\"aut\", \"cre\"), email = \"danielturek@gmail.com\"),\nperson(\"Mark\", \"Risser\", role = \"aut\"))", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-10 08:19:52 UTC", "User": "root" }, "Author": "Daniel Turek [aut, cre], Mark Risser [aut]", "Config/pak/sysreqs": "libglpk-dev make libxml2-dev", "Repository": "https://danielturek.r-universe.dev", "Date/Publication": "2025-12-11 16:50:55 UTC", "RemoteUrl": "https://github.com/cran/BayesNSGP", "RemoteRef": "HEAD", "RemoteSha": "25753e5c2e891be465f44e2f757934dcb9ef1e33", "MD5sum": "1a6fcd94ced94c496822288a1865c023", "_user": "danielturek", "_type": "src", "_file": "BayesNSGP_0.2.0.tar.gz", "_fileid": "70f3128da6c42a6690025a1a88b6dc9deb8a4d859c8a86fde51452b91e33d746", "_filesize": 214237, "_sha256": "70f3128da6c42a6690025a1a88b6dc9deb8a4d859c8a86fde51452b91e33d746", "_created": "2026-05-10T08:19:52.000Z", "_published": "2026-05-22T12:21:41.244Z", "_distro": "noble", "_jobs": [ { "job": 77378229794, "time": 173, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6902271416" }, 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"count": 2, "uuid": 4511457 } ], "_userbio": { "uuid": 4511457, "type": "user", "name": "Daniel Turek" }, "_downloads": { "count": 233, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BayesNSGP" }, "_searchresults": 36, "_rbuild": "4.6.0", "_assets": [ "extra/BayesNSGP.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "manual.pdf" ], "_realowner": "danielturek", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2019-08-29" }, { "version": "0.1.1", "date": "2019-10-12" }, { "version": "0.1.2", "date": "2022-01-09" }, { "version": "0.2.0", "date": "2025-12-11" } ], "_exports": [ "calcQF", "calculateAD_ns", "calculateU_ns", "conditionLatentObs", "crossCy_sm", "Cy_sm", "determineNeighbors", "dmnorm_gp2Scale", "dmnorm_nngp", "dmnorm_sgv", "inverseEigen", "matern_corr", "nimble_sparse_chol", "nimble_sparse_cholesky", "nimble_sparse_crossprod", "nimble_sparse_solve", "nimble_sparse_solveMat", 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"title": "Calculate sparse kernel, core kernel, and determine nonzero entries", "topics": [ "Cy_sm" ] }, { "page": "determineNeighbors", "title": "Determine the k-nearest neighbors for each spatial coordinate.", "topics": [ "determineNeighbors" ] }, { "page": "dmnorm_gp2Scale", "title": "Function for the evaluating the Gaussian likelihood with gp2Scale sparse covariance.", "topics": [ "dmnorm_gp2Scale" ] }, { "page": "dmnorm_nngp", "title": "Function for the evaluating the NNGP approximate density.", "topics": [ "dmnorm_nngp" ] }, { "page": "dmnorm_sgv", "title": "Function for the evaluating the SGV approximate density.", "topics": [ "dmnorm_sgv" ] }, { "page": "inverseEigen", "title": "Calculate covariance elements based on eigendecomposition components", "topics": [ "inverseEigen" ] }, { "page": "matern_corr", "title": "Calculate a stationary Matern correlation matrix", "topics": [ "matern_corr" ] }, { "page": "nimble_sparse_chol", "title": "nimble_sparse_chol", "topics": [ "nimble_sparse_chol" ] }, { "page": "nimble_sparse_cholesky", "title": "nimble_sparse_chol", "topics": [ "nimble_sparse_cholesky" ] }, { "page": "nimble_sparse_crossprod", "title": "nimble_sparse_crossprod", "topics": [ "nimble_sparse_crossprod" ] }, { "page": "nimble_sparse_solve", "title": "nimble_sparse_solve", "topics": [ "nimble_sparse_solve" ] }, { "page": "nimble_sparse_solveMat", "title": "nimble_sparse_crossprod", "topics": [ "nimble_sparse_solveMat" ] }, { "page": "nimble_sparse_tcrossprod", "title": "nimble_sparse_tcrossprod", "topics": [ "nimble_sparse_tcrossprod" ] }, { "page": "nsCorr", "title": "Calculate a nonstationary Matern correlation matrix", "topics": [ "nsCorr" ] }, { "page": "nsCrosscorr", "title": "Calculate a nonstationary Matern cross-correlation matrix", "topics": [ "nsCrosscorr" ] }, { "page": "nsCrossdist", "title": "Calculate coordinate-specific cross-distance matrices", "topics": [ "nsCrossdist" ] }, { "page": "nsCrossdist3d", "title": "Calculate coordinate-specific cross-distance matrices, only for nearest neighbors and store in an array", "topics": [ "nsCrossdist3d" ] }, { "page": "nsDist", "title": "Calculate coordinate-specific distance matrices", "topics": [ "nsDist" ] }, { "page": "nsDist3d", "title": "Calculate coordinate-specific distance matrices, only for nearest neighbors and store in an array", "topics": [ "nsDist3d" ] }, { "page": "nsgpModel", "title": "NIMBLE code for a generic nonstationary GP model", "topics": [ "nsgpModel" ] }, { "page": "nsgpPredict", "title": "Posterior prediction for the NSGP", "topics": [ "nsgpPredict" ] }, { "page": "orderCoordinatesMMD", "title": "Order coordinates according to a maximum-minimum distance criterion.", "topics": [ "orderCoordinatesMMD" ] }, { "page": "R_sparse_chol", "title": "R_sparse_chol", "topics": [ "R_sparse_chol" ] }, { "page": "R_sparse_cholesky", "title": "R_sparse_chol", "topics": [ "R_sparse_cholesky" ] }, { "page": "R_sparse_crossprod", "title": 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