{ "_id": "6a227f4ccd65a98ecbd50c0d", "Package": "datanugget", "Type": "Package", "Title": "Create, and Refine Data Nuggets", "Version": "1.4.0", "Date": "2025-12-01", "Authors@R": "c(\nperson(\"Rituparna\", \"Dey\", email = \"rituparnadey525@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Yajie\", \"Duan\", email = \"yajieritaduan@gmail.com\", role = \"aut\"),\nperson(\"Traymon\", \"Beavers\", email = \"tray.beavers@gmail.com\", role = \"aut\"),\nperson(\"Javier\", \"Cabrera\", email = \"xavier.cabrera@gmail.com\", role = \"aut\"),\nperson(\"Ge\", \"Cheng\", email = \"ge.cheng@outlook.com\", role = \"aut\"),\nperson(\"Kunting\", \"Qi\", email = \"qik@miamioh.edu\", role = \"aut\") ,\nperson(\"Mariusz\", \"Lubomirski\", email = \"mariusz_lubomirski@yahoo.com\", role = \"aut\")\n)", "Author": "Rituparna Dey [aut, cre], Yajie Duan [aut], Traymon Beavers\n[aut], Javier Cabrera [aut], Ge Cheng [aut], Kunting Qi [aut],\nMariusz Lubomirski [aut]", "Maintainer": "Rituparna Dey ", "Description": "Creating, and refining data nuggets. Data nuggets reduce a\nlarge dataset into a small collection of nuggets of data, each\ncontaining a center (location), weight (importance), and scale\n(variability) parameter. Data nugget centers are created by\nchoosing observations in the dataset which are as equally\nspaced apart as possible. Data nugget weights are created by\ncounting the number observations closest to a given data nugget\ncenter. We then say the data nugget 'contains' these\nobservations and the data nugget center is recalculated as the\nmean of these observations. Data nugget scales are created by\ncalculating the trace of the covariance matrix of the\nobservations contained within a data nugget divided by the\ndimension of the dataset. Data nuggets are refined by\n'splitting' data nuggets which have scales or shapes (defined\nas the ratio of the two largest eigenvalues of the covariance\nmatrix of the observations contained within the data nugget)\nReference paper: [1] Beavers, T. E., Cheng, G., Duan, Y.,\nCabrera, J., Lubomirski, M., Amaratunga, D., & Teigler, J. E.\n(2024). Data Nuggets: A Method for Reducing Big Data While\nPreserving Data Structure. Journal of Computational and\nGraphical Statistics, 1-21. [2] Cherasia, K. E., Cabrera, J.,\nFernholz, L. T., & Fernholz, R. (2022). Data Nuggets in\nSupervised Learning. \\emph{In Robust and Multivariate\nStatistical Methods: Festschrift in Honor of David E. Tyler}\n(pp. 429-449). 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