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  "Title": "Probabilistic Streaming Data Sketches",
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  "Authors@R": "c(\nperson(\"Pedro\", \"Baltazar\", email = \"pedrobtz@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\")),\nperson(\"The Apache Software Foundation\", role = \"ctb\",\ncomment = \"Author of bundled Apache DataSketches C++ code\"),\nperson(\"Stephan\", \"Brumme\", role = \"ctb\",\ncomment = \"Author of bundled xxhash64.h code\"),\nperson(\"Austin\", \"Appleby\", role = \"ctb\",\ncomment = \"Author of bundled public-domain MurmurHash3 code\"),\nperson(c(\"Sean\", \"Eron\"), \"Anderson\", role = \"ctb\",\ncomment = \"Author of bundled public-domain bit-hack code used in ceiling_power_of_2.hpp\")\n)",
  "Description": "Provides an interface to the 'Apache DataSketches'\n(<https://datasketches.apache.org/>) library of streaming\nalgorithms for approximate analytics on data too large to hold\nor process exactly. Sketches are compact, mergeable summaries\nbuilt in a single pass over a stream that answer queries such\nas approximate distinct counts, quantiles and ranks, frequent\nitems and point-frequency estimates, weighted sampling, and set\nmembership with mathematically proven error bounds. Implements\nKarnin-Lang-Liberty (KLL), Relative Error Quantiles (REQ),\nt-Digest, HyperLogLog (HLL), Compressed Probabilistic Counting\n(CPC), Theta, Frequent Items, Count-Min, Array of Doubles,\nVariance Optimal (VarOpt), Exact and Bounded Probabilistic\nProportional-to-Size (EBPPS), and Bloom filter sketches, with\nnative serialization for interoperability with other 'Apache\nDataSketches' implementations.",
  "License": "MIT + file LICENSE",
  "Copyright": "file inst/COPYRIGHTS",
  "URL": "https://github.com/pedrobtz/data.sketches,\nhttps://pedrobtz.github.io/data.sketches/",
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      "title": "Array of Doubles (Tuple) sketch for estimating sums alongside distinct counts",
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    },
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      "title": "Array of Doubles sketch set operations",
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        "array_of_doubles_intersection",
        "array_of_doubles_set_operations",
        "array_of_doubles_union"
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    },
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      "title": "Bloom filter for approximate set membership",
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    },
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      "title": "Suggest Bloom filter sizing parameters",
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        "bloom_filter_suggest_num_filter_bits",
        "bloom_filter_suggest_num_hashes"
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    },
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        "count_min_suggest_num_buckets",
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      "title": "Frequent Items sketch for approximate frequency estimation",
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      "title": "HLL sketch for approximate distinct counting",
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