{
  "_id": "6a1ef606b401979e73416fdc",
  "Package": "SpTe2M",
  "Type": "Package",
  "Title": "Nonparametric Modeling and Monitoring of Spatio-Temporal Data",
  "Version": "1.0.3",
  "Authors@R": "c(person(\"Kai\",  \"Yang\", role = c(\"aut\", \"cre\"), email = \"kayang@mcw.edu\"),\nperson(\"Peihua\", \"Qiu\", role = \"ctb\"))",
  "Date": "2023-9-29",
  "Description": "Spatio-temporal data have become increasingly popular in\nmany research fields. Such data often have complex structures\nthat are difficult to describe and estimate. This package\nprovides reliable tools for modeling complicated\nspatio-temporal data. It also includes tools of online process\nmonitoring to detect possible change-points in a\nspatio-temporal process over time. More specifically, the\npackage implements the spatio-temporal mean estimation\nprocedure described in Yang and Qiu (2018)\n<doi:10.1002/sim.7622>, the spatio-temporal covariance\nestimation procedure discussed in Yang and Qiu (2019)\n<doi:10.1002/sim.8315>, the three-step method for the joint\nestimation of spatio-temporal mean and covariance functions\nsuggested by Yang and Qiu (2022)\n<doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease\nsurveillance method discussed in Qiu and Yang (2021)\n<doi:10.1002/sim.9150> that can accommodate the covariate\neffect, the spatial-LASSO-based process monitoring method\nproposed by Qiu and Yang (2023)\n<doi:10.1080/00224065.2022.2081104>, and the online\nspatio-temporal disease surveillance method described in Yang\nand Qiu (2020) <doi:10.1080/24725854.2019.1696496>.",
  "License": "GPL (>= 3)",
  "NeedsCompilation": "yes",
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  "Author": "Kai Yang [aut, cre], Peihua Qiu [ctb]",
  "Maintainer": "Kai Yang <kayang@mcw.edu>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2023-09-30 03:31:01 UTC",
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      "page": "SpTe2M-package",
      "title": "Nonparametric Modeling and Monitoring of Spatio-Temporal Data",
      "topics": [
        "SpTe2M-package",
        "SpTe2M"
      ]
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      "page": "cv_mspe",
      "title": "Cross-validation mean squared prediction error",
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      ]
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    {
      "page": "ili_dat",
      "title": "Florida influenza-like illness data",
      "topics": [
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      ]
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    {
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      "title": "PM2.5 concentration data",
      "topics": [
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      ]
    },
    {
      "page": "sim_dat",
      "title": "A simulated spatio-temporal dataset",
      "topics": [
        "sim_dat"
      ]
    },
    {
      "page": "spte_covest",
      "title": "Estimate the spatio-temporal covariance function",
      "topics": [
        "spte_covest"
      ]
    },
    {
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      "title": "Decorrelate the spatio-temporal data",
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      "title": "Estimate the spatio-temporal mean function",
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      "title": "SpTe2M: Nonparametric Modeling and Monitoring of Spatio-Temporal Data",
      "author": "Kai Yang, Peihua Qiu",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Florida ILI Data",
        "Load ILI data",
        "Create ILI maps",
        "ILI Data Modeling",
        "Estimate spatio-temporal mean",
        "Estimate spatio-temporal covariance",
        "ILI Data Monitoring",
        "Use the function sptemnt_cusum()",
        "Use the function sptemnt_ewmac()",
        "Use the function sptemnt_ewsl()",
        "References"
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      "modified": "2023-09-30 03:31:01",
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