{ "_id": "6a12acf5acfb0bcc41d18401", "Package": "remotePARTS", "Title": "Spatiotemporal Autoregression Analyses for Large Data Sets", "Version": "1.1", "Authors@R": "c(person(given = \"Clay\",\nfamily = \"Morrow\",\nrole = c(\"aut\", \"cre\"),\nemail = \"morrowcj@outlook.com\",\ncomment = c(ORCID = \"0000-0002-3069-3296\")),\nperson(given = \"Anthony\",\nfamily = \"Ives\",\nrole = c(\"aut\"),\nemail = \"arives@wisc.edu\",\ncomment = c(ORCID = \"0000-0001-9375-9523\")))", "Description": "These tools were created to test map-scale hypotheses\nabout trends in large remotely sensed data sets but any data\nwith spatial and temporal variation can be analyzed. Tests are\nconducted using the PARTS method for analyzing spatially\nautocorrelated time series (Ives et al., 2021:\n). The method's unique approach\ncan handle extremely large data sets that other spatiotemporal\nmodels cannot, while still appropriately accounting for spatial\nand temporal autocorrelation. This is done by partitioning the\ndata into smaller chunks, analyzing chunks separately and then\ncombining the separate analyses into a single, correlated test\nof the map-scale hypotheses.", "URL": "https://github.com/morrowcj/remotePARTS", "BugReports": "https://github.com/morrowcj/remotePARTS/issues", "License": "GPL (>= 3)", "Encoding": "UTF-8", "LazyData": "TRUE", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "Repository": "https://morrowcj.r-universe.dev", "Date/Publication": "2025-07-25 06:42:13 UTC", "RemoteUrl": "https://github.com/morrowcj/remoteparts", "RemoteRef": "HEAD", "RemoteSha": "c2391d50e33edc51b0aa515d459d5c1ad03b8dfc", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-24 07:40:41 UTC", "User": "root" }, "Author": "Clay Morrow [aut, cre] (ORCID: ),\nAnthony Ives [aut] (ORCID: )", "Maintainer": "Clay Morrow ", "MD5sum": "81e665ecbdd9c9e0015297c0490aa3bc", "_user": "morrowcj", "_type": "src", "_file": "remotePARTS_1.1.tar.gz", "_fileid": 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