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  "Title": "Cluster Analysis Using Thresholding After Random Projections\n(n-TARP)",
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  "Description": "Implements the high-dimensional clustering technique\nThresholding After Random Projections (n-TARP). Provides\nfunctionality to iteratively decompose larger datasets using\ncontextual variables or within-cluster sum of squares. See\nTarun & Boutin (2018) <doi:10.48550/arXiv.1806.05297> and Tarun\n& Boutin (2018) <doi:10.4231/R74B2ZJV> for the original method\nand applications.",
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  "Maintainer": "David Reeping <reepindp@ucmail.uc.edu>",
  "Repository": "https://cran.r-universe.dev",
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      "title": "Combine nTARP BestCluster solutions into one table and assign stable final cluster IDs",
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    {
      "page": "nTARP",
      "title": "Thresholding After Random Projections (n-TARP) Clustering",
      "topics": [
        "nTARP"
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    {
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      "title": "Run nTARP repeatedly in a bisecting fashion",
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      "title": "The nTARP Package",
      "author": "David Reeping",
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      "headings": [
        "1. Introduction to the n-TARP clustering method",
        "1.1 Installing the package and running the core n-TARP function",
        "1.1 Getting the optimal solution from the nTARP function",
        "1.2 Using the results of nTARP as a classifier",
        "1.3 Checking the clusterability of the dataset",
        "1.4 The last outputs of the nTARP function",
        "2. Extending nTARP to form multiple clusters"
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      "modified": "2026-03-20 13:37:49",
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