{
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  "Package": "klic",
  "Title": "Kernel Learning Integrative Clustering",
  "Version": "1.0.4",
  "Authors@R": "c(person(given = \"Alessandra\", family = \"Cabassi\", email = \"alessandra.cabassi@mrc-bsu.cam.ac.uk\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1605-652X\")),\nperson(given = \"Paul DW\", family = \"Kirk\", role = c(\"ths\"), comment = c(ORCID = \"0000-0002-5931-7489\")),\nperson(given = \"Mehmet\", family = \"Gonen\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0002-2483-075X\")))",
  "Author": "Alessandra Cabassi [aut, cre]\n(<https://orcid.org/0000-0003-1605-652X>), Paul DW Kirk [ths]\n(<https://orcid.org/0000-0002-5931-7489>), Mehmet Gonen [ctb]\n(<https://orcid.org/0000-0002-2483-075X>)",
  "Description": "Kernel Learning Integrative Clustering (KLIC) is an\nalgorithm that allows to combine multiple kernels, each\nrepresenting a different measure of the similarity between a\nset of observations. The contribution of each kernel on the\nfinal clustering is weighted according to the amount of\ninformation carried by it. As well as providing the functions\nrequired to perform the kernel-based clustering, this package\nalso allows the user to simply give the data as input: the\nkernels are then built using consensus clustering. Different\nstrategies to choose the best number of clusters are also\navailable. For further details please see Cabassi and Kirk\n(2020) <doi:10.1093/bioinformatics/btaa593>.",
  "License": "MIT + file LICENSE",
  "URL": "http://github.com/acabassi/klic",
  "BugReports": "http://github.com/acabassi/klic/issues",
  "Encoding": "UTF-8",
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  "Maintainer": "Alessandra Cabassi <alessandra.cabassi@mrc-bsu.cam.ac.uk>",
  "Config/pak/sysreqs": "libicu-dev",
  "Repository": "https://cranhaven.r-universe.dev",
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    "r-universe"
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      "page": "klic",
      "title": "Kernel learning integrative clustering",
      "topics": [
        "klic"
      ]
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      "title": "Localised multiple kernel k-means",
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