{ "_id": "6a1f1a9db401979e7341f1e9", "Package": "spsurvey", "Title": "Spatial Sampling Design and Analysis", "Version": "5.6.1", "Authors@R": "c(\nperson(\"Michael\", \"Dumelle\", role=c(\"aut\",\"cre\"),\nemail = \"Dumelle.Michael@epa.gov\", comment = c(ORCID = \"0000-0002-3393-5529\")),\nperson(\"Tom\", \"Kincaid\", role=\"aut\"),\nperson(\"Anthony (Tony) R.\", \"Olsen\", role=\"aut\"),\nperson(\"Marc\", \"Weber\", role=\"aut\"),\nperson(\"Don\", \"Stevens\", role=\"ctb\"),\nperson(\"Denis\", \"White\", role=\"ctb\"),\nperson(\"Amanda M.\", \"Nahlik\", role=\"ctb\"),\nperson(\"Sarah\", \"Lehmann\", role=\"ctb\")\n)", "Maintainer": "Michael Dumelle ", "Description": "A design-based approach to statistical inference, with a\nfocus on spatial data. Spatially balanced samples are selected\nusing the Generalized Random Tessellation Stratified (GRTS)\nalgorithm. The GRTS algorithm can be applied to finite\nresources (point geometries) and infinite resources (linear /\nlinestring and areal / polygon geometries) and flexibly\naccommodates a diverse set of sampling design features,\nincluding stratification, unequal inclusion probabilities,\nproportional (to size) inclusion probabilities, legacy\n(historical) sites, a minimum distance between sites, and two\noptions for replacement sites (reverse hierarchical order and\nnearest neighbor). Data are analyzed using a wide range of\nanalysis functions that perform categorical variable analysis,\ncontinuous variable analysis, attributable risk analysis, risk\ndifference analysis, relative risk analysis, change analysis,\nand trend analysis. spsurvey can also be used to summarize\nobjects, visualize objects, select samples that are not\nspatially balanced, select panel samples, measure the amount of\nspatial balance in a sample, adjust design weights, and more.\nFor additional details, see Dumelle et al. (2023)\n.", "License": "GPL (>= 3)", "URL": "https://usepa.github.io/spsurvey/,\nhttps://github.com/USEPA/spsurvey", "BugReports": "https://github.com/USEPA/spsurvey/issues", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "NeedsCompilation": "no", "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nmake libssl-dev libproj-dev libsqlite3-dev libudunits2-dev", "Repository": "https://usepa.r-universe.dev", "Date/Publication": "2026-05-15 18:15:12 UTC", "RemoteUrl": "https://github.com/usepa/spsurvey", "RemoteRef": "HEAD", "RemoteSha": "0914fcd071713fdab19f43d8d9a66436830bd917", "Packaged": { "Date": "2026-05-16 09:36:00 UTC", "User": "root" }, "Author": "Michael Dumelle [aut, cre] (ORCID:\n),\nTom Kincaid [aut],\nAnthony (Tony) R. Olsen [aut],\nMarc Weber [aut],\nDon Stevens [ctb],\nDenis White [ctb],\nAmanda M. Nahlik [ctb],\nSarah Lehmann [ctb]", "MD5sum": "b3c8241fe01103562531c5398d23be31", "_user": "usepa", "_type": "src", "_file": "spsurvey_5.6.1.tar.gz", "_fileid": "9cc6705e748546fd8d056773da7a4abb43adc150f193505ce17c41a89fd02f12", "_filesize": 1340637, "_sha256": "9cc6705e748546fd8d056773da7a4abb43adc150f193505ce17c41a89fd02f12", "_created": "2026-05-16T09:36:00.000Z", "_published": "2026-06-02T18:02:05.492Z", "_distro": "noble", "_jobs": [ { "job": 79138227103, "time": 251, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7032385523" }, { "job": 79138226905, "time": 242, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7032384693" }, { "job": 79138226937, "time": 152, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7032374767" }, { "job": 79138226940, "time": 146, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7032373930" }, { "job": 79138226565, "time": 226, "config": "source", 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"Michael Dumelle", "email": "dumelle.michael@epa.gov", "login": "michaeldumelle", "uuid": 54114566, "orcid": "0000-0002-3393-5529" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "sf", "role": "Depends" }, { "package": "survey", "version": ">= 4.1-1", "role": "Depends" }, { "package": "boot", "role": "Imports" }, { "package": "crossdes", "role": "Imports" }, { "package": "deldir", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "lme4", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "sampling", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "units", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "testthat", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" } ], "_owner": "usepa", "_selfowned": true, "_usedby": 2, "_updates": [ { 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cumulative distribution function (CDF)", "topics": [ "cdf_plot" ] }, { "page": "change_analysis", "title": "Change analysis", "topics": [ "change_analysis" ] }, { "page": "cont_analysis", "title": "Continuous variable analysis", "topics": [ "cont_analysis" ] }, { "page": "cont_cdfplot", "title": "Create a PDF file containing cumulative distribution functions (CDF) plots", "topics": [ "cont_cdfplot" ] }, { "page": "cont_cdftest", "title": "Cumulative distribution function (CDF) inference for a probability survey", "topics": [ "cont_cdftest" ] }, { "page": "cov_panel_dsgn", "title": "Create a covariance matrix for a panel design", "topics": [ "cov_panel_dsgn" ] }, { "page": "diffrisk_analysis", "title": "Risk difference analysis", "topics": [ "diffrisk_analysis" ] }, { "page": "errorprnt", "title": "Print errors from analysis functions", "topics": [ "errorprnt" ] }, { "page": "grts", "title": "Select a generalized random tessellation stratified (GRTS) sample", "topics": [ "grts" ] }, { "page": "Illinois_River", "title": "Illinois River data", "topics": [ "Illinois_River" ] }, { "page": "Illinois_River_Legacy", "title": "Illinois River legacy data", "topics": [ "Illinois_River_Legacy" ] }, { "page": "irs", "title": "Select an independent random sample (IRS)", "topics": [ "irs" ] }, { "page": "Lake_Ontario", "title": "Lake Ontario data", "topics": [ "Lake_Ontario" ] }, { "page": "localmean_cov", "title": "Internal Function: Variance-Covariance Matrix Based on Local Mean Estimator", "topics": [ "localmean_cov" ] }, { "page": "localmean_var", "title": "Internal Function: Local Mean Variance Estimator", "topics": [ "localmean_var" ] }, { "page": "localmean_weight", "title": "Internal Function: Local Mean Variance Neighbors and Weights", "topics": [ "localmean_weight" ] }, { "page": "NE_Lakes", "title": "New England Lakes data", "topics": [ "NE_Lakes" ] }, { "page": "NE_Lakes_df", "title": "New England Lakes data (as a data frame)", "topics": [ "NE_Lakes_df" ] }, { "page": 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