{ "_id": "6a268820794753ddbc3f2894", "Package": "DSWE", "Title": "Data Science for Wind Energy", "Version": "1.8.4", "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) , AMK() - Lee et al.\n(2015) , tempGP() - Prakash\net al. (2022) ,\nComparePCurve() - Ding et al. (2021)\n, deltaEnergy() - Latiffianti\net al. (2022) , syncSize() - Latiffianti\net al. (2022) , imptPower() - Latiffianti\net al. (2022) , 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", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-08 09:09:46 UTC", "User": "root" }, "Author": "Nitesh Kumar [aut], Abhinav Prakash [aut], Yu Ding [aut, cre],\nEffi Latiffianti [ctb, cph], Ahmadreza Chokhachian [ctb, cph]", "Maintainer": "Yu Ding ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-10-11 21:00:02 UTC", "RemoteUrl": "https://github.com/cran/DSWE", "RemoteRef": "HEAD", "RemoteSha": "e23c236ef0f5272a6f48c2dfab8b5001f3702a29", "MD5sum": "dad980eae343f67b17622aaf728e8701", "_user": "cran", "_type": "src", "_file": "DSWE_1.8.4.tar.gz", "_fileid": "d8a9762b2d2b4e0120560500e03efa4e00d40d555a8ccd14d6011572f06c20cc", "_filesize": 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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" ] }, { "page": "imptPower", "title": "Power imputation", "topics": [ "imptPower" ] }, { "page": "KnnPCFit", "title": "KNN : Fit", "topics": [ "KnnPCFit" ] }, { "page": "KnnPredict", "title": "KNN : Predict", "topics": [ "KnnPredict" ] }, { "page": "KnnUpdate", "title": "KNN : Update", "topics": [ "KnnUpdate" ] }, { "page": "predict.tempGP", "title": "predict from temporal Gaussian process", "topics": [ 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