{ "_id": "6a1d69ba1d7bb097a0a4ef4e", "Package": "remotePARTS", "Title": "Spatiotemporal Autoregression Analyses for Large Data Sets", "Version": "1.0.4", "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": "7.2.3", "VignetteBuilder": "knitr", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-01 11:10:12 UTC", "User": "root" }, "Author": "Clay Morrow [aut, cre]\n(), Anthony Ives [aut]\n()", "Maintainer": "Clay Morrow ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2023-09-15 21:30:38 UTC", "RemoteUrl": "https://github.com/cran/remotePARTS", "RemoteRef": "HEAD", "RemoteSha": "aa1aa997c7cd3a0cdc534da3ebcd1ffba03bd16c", "MD5sum": "9576ffcf33bde3596ecd845332ad64df", "_user": "cran", "_type": "src", "_file": "remotePARTS_1.0.4.tar.gz", "_fileid": 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"check_posdef" ] }, { "page": "chisqr", "title": "Conduct a chi-squared test", "topics": [ "chisqr" ] }, { "page": "chisqr.partGLS", "title": "Conduct a chisqr test of \"partGLS\" object", "topics": [ "chisqr.partGLS" ] }, { "page": "covar_functions", "title": "Tapered-spherical distance-based covariance function", "topics": [ "covar_exp", "covar_exppow", "covar_taper" ] }, { "page": "crosspart_GLS", "title": "Calculate cross-partition statistics in a partitioned GLS", "concept": [ "partitionedGLS" ], "topics": [ "crosspart_GLS" ] }, { "page": "distm_scaled", "title": "Calculate a distance matrix from coordinates", "topics": [ "distm_km", "distm_scaled" ] }, { "page": "fitAR", "title": "AR regressions by REML", "concept": [ "remoteTS" ], "topics": [ "AR_fun", "fitAR" ] }, { "page": "fitAR_map", "title": "Map-level AR REML", "concept": [ "remoteTS" ], "topics": [ "fitAR_map" ] }, { "page": "fitCLS", "title": "CLS for time series", "concept": [ "remoteTS" ], "topics": [ "fitCLS" ] }, { "page": "fitCLS_map", "title": "Map-level CLS for time series", "concept": [ "remoteTS" ], "topics": [ "fitCLS_map" ] }, { "page": "fitCor", "title": "Estimate spatial parameters from time series residuals", "topics": [ "fitCor" ] }, { "page": "fitGLS", "title": "Fit a PARTS GLS model.", "topics": [ "fitGLS" ] }, { "page": "fitGLS_opt", "title": "Fit a PARTS GLS model, with maximum likelihood spatial parameters", "topics": [ "fitGLS_opt" ] }, { "page": "fitGLS_opt_FUN", "title": "Function that fitGLS_opt optimizes over", "topics": [ "fitGLS_opt_FUN" ] }, { "page": "invert_chol", "title": "Invert the cholesky decomposition of V", "topics": [ "invert_chol" ] }, { "page": "max_dist", "title": "calculate maximum distance among a table of coordinates", "topics": [ "max_dist" ] }, { "page": "partGLS", "title": "fit a parallel partitioned GLS", "concept": [ "partitionedGLS" ], "topics": [ "fitGLS_partition", "MCGLS_partsummary", "MC_GLSpart", "multicore_fitGLS_partition", "part_csv", "part_data" ] }, { "page": "ndvi_AK10000", "title": "NDVI remote sensing data for 10,000 random pixels from Alaska, with rare land classes removed.", "topics": [ "ndvi_AK10000" ] }, { "page": "optimize_nugget", "title": "Find the maximum likelihood estimate of the nugget", "topics": [ "optimize_nugget" ] }, { "page": "part_chisqr", "title": "Chisqr test for partitioned GLS", "topics": [ "part_chisqr" ] }, { "page": "part_ttest", "title": "Correlated t-test for paritioned GLS", "topics": [ "part_ttest" ] }, { "page": "partGLS_ndviAK", "title": "partitioned GLS results", "topics": [ "partGLS_ndviAK" ] }, { "page": "print.partGLS", "title": "S3 print method for \"partGLS\" objects", "topics": [ "print.partGLS" ] }, { "page": "print.remoteCor", "title": "S3 print method for \"remoteCor\" class", "topics": [ "print.remoteCor" ] }, { "page": "print.remoteGLS", "title": "print method for remoteGLS", "topics": [ "print.remoteGLS" ] }, { "page": "TS.methods", "title": "S3 print method for 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