{ "_id": "6a1bea0e1d7bb097a0a1e032", "Package": "mrtsSphere", "Type": "Package", "Title": "Multi-Resolution Thin-Plate Splines on the Sphere", "Version": "0.2.0", "Authors@R": "c(\nperson(given = \"Hao-Yun\", family = \"Huang\", role = c(\"aut\", \"cre\"),\nemail = \"hhuscout@gmail.com\",\ncomment = c(ORCID = \"0009-0004-8384-3261\")),\nperson(given = \"Hsin-Cheng\", family = \"Huang\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-5613-349X\")),\nperson(given = \"Ching-Kang\", family = \"Ing\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-1362-8246\")))", "Description": "Constructs multi-resolution thin-plate spline (MRTS) basis\nfunctions on the sphere and fits the corresponding\nmulti-resolution mixed model (MRMM) by generalised least\nsquares with a tapered Matérn residual covariance, including\nkriging-style prediction with closed-form standard errors.\nImplements the system described in Huang, Huang, and Ing (2025)\n\"Multi-Resolution Spatial Methods on the Sphere: Efficient\nPrediction for Global Data\", Environmetrics,\n. Heavy basis computations are written\nin 'C++' via 'Rcpp' with optional 'OpenMP' parallelism.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "SystemRequirements": "GNU make", "RoxygenNote": "7.3.2", "Roxygen": "list(markdown = TRUE)", "URL": "https://github.com/STLABTW/multi-resolution-sphere", "BugReports": "https://github.com/STLABTW/multi-resolution-sphere/issues", "Config/testthat/edition": "3", "Config/pak/sysreqs": "make", "Repository": "https://stlabtw.r-universe.dev", "Date/Publication": "2026-05-01 08:24:18 UTC", "RemoteUrl": "https://github.com/stlabtw/multi-resolution-sphere", "RemoteRef": "HEAD", "RemoteSha": "206bccb5d8ddaf0f5cd56b31c93cf31121e18d11", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-31 07:09:23 UTC", "User": "root" }, "Author": "Hao-Yun Huang [aut, cre] (ORCID:\n),\nHsin-Cheng Huang [aut] (ORCID: ),\nChing-Kang Ing [aut] (ORCID: )", "Maintainer": "Hao-Yun Huang ", "MD5sum": "7a5fe9d4732ef35b12d300c5d09443de", "_user": "stlabtw", "_type": "src", "_file": "mrtsSphere_0.2.0.tar.gz", "_fileid": "d21f74e86691dc12f51e0a5b21c21c31c5fc1864bc68a58aaba3415720ba65f0", "_filesize": 1928492, "_sha256": "d21f74e86691dc12f51e0a5b21c21c31c5fc1864bc68a58aaba3415720ba65f0", "_created": "2026-05-31T07:09:23.000Z", "_published": "2026-05-31T07:58:06.046Z", "_distro": "noble", "_jobs": [ { "job": 78707717774, "time": 153, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "7314697990" }, { "job": 78707717753, "time": 151, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7314697715" }, { "job": 78707717757, "time": 182, "config": "linux-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7314701253" }, { "job": 78707717758, "time": 155, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7314698139" }, { "job": 78707717775, "time": 260, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "" }, { "job": 78707717804, "time": 263, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "7314740696" }, { "job": 78707717781, "time": 127, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7314977332" }, { "job": 78707717809, "time": 372, "config": "macos-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7314774551" }, { "job": 78707560011, "time": 173, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7314681534" }, { "job": 78707717738, "time": 126, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7314694946" }, { "job": 78707717762, "time": 130, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7314695509" }, { "job": 78707717755, "time": 125, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7314695076" }, { "job": 78707717770, "time": 111, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7314693392" } ], "_buildurl": "https://github.com/r-universe/stlabtw/actions/runs/26706100573", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/stlabtw/multi-resolution-sphere", "_commit": { "id": "206bccb5d8ddaf0f5cd56b31c93cf31121e18d11", "author": "Hao-Yun Huang ", "committer": "Hao-Yun Huang ", "message": "mrtsSphere 0.2.0: add MRMM fit + predict\n\nThe 0.1.x line shipped only the basis-construction step (`mrts_sphere`),\nwhich left users to write the GLS step (the actual modelling part of\nthe paper) themselves. 0.2.0 adds it.\n\nNew exports:\n\n- `mrmm_fit(z, loc, knots, k, taper_range, matern_range, sill, nugget,\n smoothness)` — fits the multi-resolution mixed model by GLS using a\n tapered-Matérn residual covariance built from the user-supplied\n parameters. Returns an S3 `\"mrmm\"` object holding the GLS coefficient,\n fitted values, residuals, the (B' Sigma^{-1} B)^{-1} matrix needed\n for prediction SE, and the upper-triangular Cholesky of Sigma so\n triangular solves can be reused at predict time.\n- `predict.mrmm(object, newdata, se.fit = TRUE)` — kriging-style mean\n prediction plus closed-form prediction standard error.\n- `tapered_matern_sphere(loc1, loc2, taper_range, matern_range, ...)` —\n exported helper that returns\n `sill * Matern(d; c, nu) * Wendland(d; a) + nugget * I` on the unit\n sphere, with `d` the great-circle distance in radians.\n- `print.mrmm()` for human-readable summaries.\n\nImplementation notes:\n\n- All linear algebra is base R + dense matrices, factorising Sigma\n once via `chol()` and reusing the factor for every triangular solve.\n This is a deliberate v0.2.0 simplification — practical training size\n is comfortable up to a few thousand points; sparse / Matrix support\n can come later if there is demand.\n- Avoids `pmin`/`pmax` against scalars in `gcdist_sphere()` because they\n silently strip the matrix `dim` attribute when the first argument is\n a scalar; uses direct `[ <- ]` indexing instead.\n- Wendland C^2 (k = 2, dim = 2) taper closed form\n `(1 - r)^6 * (35/3 r^2 + 6 r + 1)` for r <= 1, 0 outside; matches\n what `fields::Wendland(theta = a, k = 2, dimension = 2)` returns\n and what the original paper script uses.\n- Matern correlation uses `besselK()`; takes the closed-form\n `exp(-d / range)` shortcut when smoothness is 0.5.\n\nA future relea", "time": 1777623858 }, "_maintainer": { "name": "Hao-Yun Huang", "email": "hhuscout@gmail.com", "login": "hhuscout", "description": "", "uuid": 26742798 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppEigen", "role": "LinkingTo" }, { "package": "RcppNumerical", "role": "LinkingTo" }, { "package": "Rcpp", "role": "Imports" }, { "package": "RSpectra", "role": "Imports" }, { "package": "fields", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "stlabtw", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-13", "n": 4 }, { "week": "2026-14", "n": 1 }, { "week": "2026-18", "n": 7 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "hhuscout", "count": 12, "uuid": 26742798 } ], "_userbio": { "uuid": 250980567, "type": "organization", "name": "Spatio-Temporal Lab @ NDHU", "description": "Spatial statistics, spatio-temporal modeling, and deep learning for large-scale geospatial data. 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