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  "Package": "sansa",
  "Title": "Synthetic Data Generation for Imbalanced Learning in 'R'",
  "Version": "0.0.1",
  "Authors@R": "c(\nperson(given = \"Murtaza\",\nfamily = \"Nasir\",\nrole = c(\"aut\", \"cre\"),\nemail = \"mail@murtaza.cc\",\ncomment = c(ORCID = \"0000-0002-4481-065X\")),\nperson(given = \"Ali\",\nfamily = \"Dag\",\nrole = c(\"ctb\")),\nperson(given = \"Serhat\",\nfamily = \"Simsek\",\nrole = c(\"ctb\")),\nperson(given = \"Anton\",\nfamily = \"Ivanov\",\nrole = c(\"ctb\")),\nperson(given = \"Asil\",\nfamily = \"Oztekin\",\nrole = c(\"ths\"))\n)",
  "Description": "Machine learning is widely used in information-systems\ndesign. Yet, training algorithms on imbalanced datasets may\nseverely affect performance on unseen data. For example, in\nsome cases in healthcare, financial, or internet-security\ncontexts, certain sub-classes are difficult to learn because\nthey are underrepresented in training data. This 'R' package\noffers a flexible and efficient solution based on a new\nsynthetic average neighborhood sampling algorithm ('SANSA'),\nwhich, in contrast to other solutions, introduces a novel\n“placement” parameter that can be tuned to adapt to each\ndatasets unique manifestation of the imbalance. More\ninformation about the algorithm's parameters can be found at\nNasir et al. (2022) <https://murtaza.cc/SANSA/>.",
  "License": "GPL (>= 3)",
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  "Packaged": {
    "Date": "2026-10-13 12:54:16 UTC",
    "User": "root"
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  "Author": "Murtaza Nasir [aut, cre]\n(<https://orcid.org/0000-0002-4481-065X>), Ali Dag [ctb],\nSerhat Simsek [ctb], Anton Ivanov [ctb], Asil Oztekin [ths]",
  "Maintainer": "Murtaza Nasir <mail@murtaza.cc>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2022-08-23 07:40:02 UTC",
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