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  "Package": "proxyC",
  "Type": "Package",
  "Title": "Computes Proximity in Large Sparse Matrices",
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  "Authors@R": "c(\nperson(\"Kohei\", \"Watanabe\", email = \"watanabe.kohei@gmail.com\", role = c(\"cre\", \"aut\", \"cph\"),\ncomment = c(ORCID = \"0000-0001-6519-5265\")),\nperson(\"Robrecht\", \"Cannoodt\", email = \"rcannood@gmail.com\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-3641-729X\"))\n)",
  "Description": "Computes proximity between rows or columns of large\nmatrices efficiently in C++. Functions are optimised for large\nsparse matrices using the Armadillo and Intel TBB libraries.\nAmong various built-in similarity/distance measures,\ncomputation of correlation, cosine similarity, Dice coefficient\nand Euclidean distance is particularly fast.",
  "URL": "https://github.com/koheiw/proxyC, https://koheiw.github.io/proxyC/",
  "BugReports": "https://github.com/koheiw/proxyC/issues",
  "License": "GPL-3",
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  "Packaged": {
    "Date": "2026-05-31 07:32:02 UTC",
    "User": "root"
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  "Author": "Kohei Watanabe [cre, aut, cph]\n(<https://orcid.org/0000-0001-6519-5265>), Robrecht Cannoodt\n[aut] (<https://orcid.org/0000-0003-3641-729X>)",
  "Maintainer": "Kohei Watanabe <watanabe.kohei@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-04-25 12:40:06 UTC",
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    "login": "koheiw",
    "twitter": "@koheiw7",
    "description": "Data scientist specializing in political and financial text analysis",
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  "_topics": [
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    "onetbb",
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      "date": "2025-04-24"
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  "_exports": [
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    "colZeros",
    "crossprod",
    "dist",
    "mask",
    "rowSds",
    "rowZeros",
    "simil",
    "tcrossprod"
  ],
  "_help": [
    {
      "page": "colSds",
      "title": "Standard deviation of columns and rows of large matrices",
      "topics": [
        "colSds",
        "rowSds"
      ]
    },
    {
      "page": "colZeros",
      "title": "Number of zeros in columns and rows of large matrices",
      "topics": [
        "colZeros",
        "rowZeros"
      ]
    },
    {
      "page": "crossprod",
      "title": "Cross-product of large sparse matrices",
      "topics": [
        "crossprod",
        "tcrossprod"
      ]
    },
    {
      "page": "mask",
      "title": "Create a pattern matrix for masking",
      "topics": [
        "mask"
      ]
    },
    {
      "page": "simil",
      "title": "Compute similarity/distance between rows or columns of large matrices",
      "topics": [
        "dist",
        "simil"
      ]
    }
  ],
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      "source": "measures.Rmd",
      "filename": "measures.html",
      "title": "Similarity and Distance Measures in proxyC",
      "author": "Kohei Watanabe",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Notation",
        "Similarity Measures",
        "Cosine similarity (\"cosine\")",
        "Pearson correlation coefficient (\"correlation\")",
        "Jaccard similarity (\"jaccard\" and \"ejaccard\")",
        "Fuzzy Jaccard similarity (\"fjaccard\")",
        "Dice similarity (\"dice\" and \"edice\")",
        "Hamann similarity (\"hamann\")",
        "Faith similarity (\"faith\")",
        "Simple matching (\"matching\")",
        "Distance Measures",
        "Manhattan distance (\"manhattan\")",
        "Canberra distance (\"canberra\")",
        "Euclidian (\"euclidian\")",
        "Minkowski distance (\"minkowski\")",
        "Hamming distance (\"hamming\")",
        "The largest difference between values (\"maximum\")",
        "Chi-squared divergence (\"chisquared\")",
        "Kullback--Leibler divergence (\"kullback\")",
        "Jeffreys divergence (\"jeffreys\")",
        "Jensen-Shannon divergence (\"jensen\")",
        "References"
      ],
      "created": "2022-10-06 03:20:02",
      "modified": "2025-04-22 22:10:02",
      "commits": 5
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