{
  "_id": "6a1f2d5eb401979e73426505",
  "Package": "DSWE",
  "Title": "Data Science for Wind Energy",
  "Version": "1.8.0",
  "Authors@R": "c(\nperson(given = \"Nitesh\", family = \"Kumar\", role = c(\"aut\"), email = \"nkumar.tamu@gmail.com\"),\nperson(given = \"Abhinav\", family = \"Prakash\", role = c(\"aut\"), email = \"aprakashphd@gmail.com\"),\nperson(given = \"Yu\", family = \"Ding\", role = c(\"aut\",\"cre\"), email = \"yuding2007@gmail.com\"),\nperson(given = \"Effi\", family = \"Latiffianti\", role = c(\"ctb\",\"cph\"), email = \"latiffianti@gmail.com\"),\nperson(given = \"Ahmadreza\", family = \"Chokhachian\", role = c(\"ctb\",\"cph\"), email = \"ar.chokhachian@gmail.com\"))",
  "Description": "Data science methods used in wind energy applications.\nCurrent functionalities include creating a multi-dimensional\npower curve model, performing power curve function comparison,\ncovariate matching, and energy decomposition. Relevant works\nfor the developed functions are: funGP() - Prakash et al.\n(2022) <doi:10.1080/00401706.2021.1905073>, AMK() - Lee et al.\n(2015) <doi:10.1080/01621459.2014.977385>, tempGP() - Prakash\net al. (2022) <doi:10.1080/00401706.2022.2069158>,\nComparePCurve() - Ding et al. (2021)\n<doi:10.1016/j.renene.2021.02.136>, deltaEnergy() - Latiffianti\net al. (2022) <doi:10.1002/we.2722>, syncSize() - Latiffianti\net al. (2022) <doi:10.1002/we.2722>, imptPower() - Latiffianti\net al. (2022) <doi:10.1002/we.2722>, All other functions - Ding\n(2019, ISBN:9780429956508).",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/TAMU-AML/DSWE-Package,\nhttps://aml.engr.tamu.edu/book-dswe/",
  "BugReports": "https://github.com/TAMU-AML/DSWE-Package/issues",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "RoxygenNote": "7.2.3",
  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev",
  "Repository": "https://tamu-aml.r-universe.dev",
  "Date/Publication": "2024-02-06 04:16:39 UTC",
  "RemoteUrl": "https://github.com/tamu-aml/dswe-package",
  "RemoteRef": "HEAD",
  "RemoteSha": "d370677c11a2f5f1cc299fa1a5454bd3e2ea5cc3",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-15 09:47:02 UTC",
    "User": "root"
  },
  "Author": "Nitesh Kumar [aut],\nAbhinav Prakash [aut],\nYu Ding [aut, cre],\nEffi Latiffianti [ctb, cph],\nAhmadreza Chokhachian [ctb, cph]",
  "Maintainer": "Yu Ding <yuding2007@gmail.com>",
  "MD5sum": "15806a2a8d13cc52b3f36a3451a31bf5",
  "_user": "tamu-aml",
  "_type": "src",
  "_file": "DSWE_1.8.0.tar.gz",
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  "_created": "2026-05-15T09:47:02.000Z",
  "_published": "2026-06-02T19:22:06.739Z",
  "_distro": "noble",
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  "_host": "GitHub-Actions",
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    "author": "Abhinav Prakash <aprakashphd@gmail.com>",
    "committer": "GitHub <noreply@github.com>",
    "message": "Merge pull request #24 from TAMU-AML/development\n\nDevelopment",
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  "_owner": "tamu-aml",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [],
  "_tags": [],
  "_stars": 11,
  "_contributors": [
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  "_userbio": {
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    "name": "Applied Machine Learning (AML) Lab",
    "description": "Yu Ding's Research Group"
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  "_mentions": 1,
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    "extra/citation.html",
    "extra/citation.json",
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  "_realowner": "tamu-aml",
  "_cranurl": true,
  "_releases": [
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      "date": "2021-01-11"
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      "date": "2022-07-08"
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      "date": "2022-07-18"
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    "ComparePCurve",
    "ComputeWeightedDifference",
    "CovMatch",
    "deltaEnergy",
    "funGP",
    "imptPower",
    "KnnPCFit",
    "KnnPredict",
    "KnnUpdate",
    "SplinePCFit",
    "SvmPCFit",
    "syncSize",
    "tempGP",
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    "XgbPCFit"
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  "_datasets": [
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      "name": "data1",
      "title": "Wind Energy data set containing 47,542 data points",
      "object": "data1",
      "class": [
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      ],
      "fields": [
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        "V",
        "D",
        "air.density",
        "I",
        "S_b",
        "Y"
      ],
      "rows": 47542,
      "table": true,
      "tojson": true
    },
    {
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      "title": "Wind Energy data set containing 48,068 data points",
      "object": "data2",
      "class": [
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      ],
      "fields": [
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        "V",
        "D",
        "air.density",
        "I",
        "S_b",
        "Y"
      ],
      "rows": 48068,
      "table": true,
      "tojson": true
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    {
      "page": "AMK",
      "title": "Additive Multiplicative Kernel Regression",
      "topics": [
        "AMK"
      ]
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    {
      "page": "ComparePCurve",
      "title": "Power curve comparison",
      "topics": [
        "ComparePCurve"
      ]
    },
    {
      "page": "ComputeWeightedDifference",
      "title": "Percentage weighted difference between power curves",
      "topics": [
        "ComputeWeightedDifference"
      ]
    },
    {
      "page": "CovMatch",
      "title": "Covariate Matching",
      "topics": [
        "CovMatch"
      ]
    },
    {
      "page": "data1",
      "title": "Wind Energy data set containing 47,542 data points",
      "topics": [
        "data1"
      ]
    },
    {
      "page": "data2",
      "title": "Wind Energy data set containing 48,068 data points",
      "topics": [
        "data2"
      ]
    },
    {
      "page": "deltaEnergy",
      "title": "Energy decomposition for wind turbine performance comparison",
      "topics": [
        "deltaEnergy"
      ]
    },
    {
      "page": "funGP",
      "title": "Function comparison using Gaussian Process and Hypothesis testing",
      "topics": [
        "funGP"
      ]
    },
    {
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      "title": "Power imputation",
      "topics": [
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      ]
    },
    {
      "page": "KnnPCFit",
      "title": "KNN : Fit",
      "topics": [
        "KnnPCFit"
      ]
    },
    {
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      "title": "KNN : Predict",
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      ]
    },
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      ]
    },
    {
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      "title": "xgboost based power curve modelling",
      "topics": [
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    }
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