{ "_id": "6a1b1d8a1d7bb097a0a0e940", "Package": "LatticeKrig", "Version": "9.4.1", "Date": "2026-05-29", "Title": "Multi-Resolution Kriging Based on Markov Random Fields", "Authors@R": "c( \nperson(\"Douglas\", \"Nychka\", role = c(\"aut\", \"cre\"),\nemail = \"nychka@mines.edu\"),\nperson(\"Dorit\", \"Hammerling\", role = c(\"aut\")),\nperson(\"Stephan\", \"Sain\", role = \"aut\"),\nperson(\"Nathan\", \"Lenssen\", role = \"aut\"),\nperson(\"Colette\", \"Smirniotis\", role = \"aut\"),\nperson(\"Matthew\", \"Iverson\", role = \"aut\"),\nperson(\"Antony\", \"Sikorski\", role = \"aut\") )", "Maintainer": "Douglas Nychka ", "Description": "Methods for the interpolation of large spatial datasets.\nThis package uses a basis function approach that provides a\nsurface fitting method that can approximate standard spatial\ndata models. Using a large number of basis functions allows for\nestimates that can come close to interpolating the observations\n(a spatial model with a small nugget variance.) Moreover, the\ncovariance model for this method can approximate the Matern\ncovariance family but also allows for a multi-resolution model\nand supports efficient computation of the profile likelihood\nfor estimating covariance parameters. This is accomplished\nthrough compactly supported basis functions and a Markov random\nfield model for the basis coefficients. These features lead to\nsparse matrices for the computations and this package makes of\nthe R spam package for sparse linear algebra. An extension of\nthis version over previous ones ( < 5.4 ) is the support for\ndifferent geometries besides a rectangular domain. The Markov\nrandom field approach combined with a basis function\nrepresentation makes the implementation of different geometries\nsimple where only a few specific R functions need to be added\nwith most of the computation and evaluation done by generic\nroutines that have been tuned to be efficient. One benefit of\nthis package's model/approach is the facility to do\nunconditional and conditional simulation of the field for large\nnumbers of arbitrary points. There is also the flexibility for\nestimating non-stationary covariances and also the case when\nthe observations are a linear combination (e.g. an integral) of\nthe spatial process. Included are generic methods for\nprediction, standard errors for prediction, plotting of the\nestimated surface and conditional and unconditional simulation.\nSee the 'LatticeKrigRPackage' GitHub repository for a vignette\nof this package. Development of this package was supported in\npart by the National Science Foundation Grant 1417857 and the\nNational Center for Atmospheric Research.", "License": "GPL (>= 2)", "URL": "https://www.r-project.org", "Packaged": { "Date": "2026-05-30 17:15:33 UTC", "User": "root" }, "NeedsCompilation": "yes", "Author": "Douglas Nychka [aut, cre], Dorit Hammerling [aut], Stephan Sain\n[aut], Nathan Lenssen [aut], Colette Smirniotis [aut], Matthew\nIverson [aut], Antony Sikorski [aut]", "Config/pak/sysreqs": "libfftw3-dev", "Repository": "https://dnychka.r-universe.dev", "Date/Publication": "2026-05-30 15:41:28 UTC", "RemoteUrl": "https://github.com/cran/LatticeKrig", "RemoteRef": "HEAD", "RemoteSha": "c10597715bfbb9d679be09086bc933f690099339", "MD5sum": "89a02582d8d693ae5a2f410b83efa625", "_user": "dnychka", "_type": "src", "_file": "LatticeKrig_9.4.1.tar.gz", "_fileid": "046d0799e1b0bb12cc0cea8a7d07a505c9f9da860780142f619edff7126b0b46", "_filesize": 773800, "_sha256": "046d0799e1b0bb12cc0cea8a7d07a505c9f9da860780142f619edff7126b0b46", "_created": "2026-05-30T17:15:33.000Z", "_published": "2026-05-30T17:25:30.720Z", "_distro": "noble", "_jobs": [ { "job": 78664702947, "time": 233, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7309953027" }, { "job": 78664702959, "time": 387, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7309969127" }, { "job": 78664702944, "time": 240, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7309953709" }, { "job": 78664702941, "time": 346, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7309964883" }, { "job": 78664702948, "time": 304, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7309958490" }, { "job": 78664702951, "time": 494, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7309979045" }, { "job": 78664702939, "time": 291, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7309956018" }, { "job": 78664702942, "time": 552, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7309984389" }, { "job": 78664502364, "time": 212, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7309928839" }, { "job": 78664702929, "time": 117, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7309941472" }, { "job": 78664702934, "time": 395, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7309970062" }, { "job": 78664702952, "time": 315, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7309962202" }, { "job": 78664702964, "time": 409, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7309971748" } ], "_buildurl": "https://github.com/r-universe/dnychka/actions/runs/26689948708", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/LatticeKrig", "_commit": { "id": "c10597715bfbb9d679be09086bc933f690099339", "author": "Douglas Nychka ", "committer": "cran-robot ", "message": "version 9.4.1\n", "time": 1780155688 }, "_maintainer": { "name": "Douglas Nychka", "email": "nychka@mines.edu", "login": "dnychka", "description": "I am data scientist interested in the application of statistical learning to interpret spatial and surface data in the environmental sciences and engineering. 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