{
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  "Package": "BIDistances",
  "Type": "Package",
  "Title": "Bioinformatic Distances",
  "Version": "1.0.1",
  "Date": "2026-08-19",
  "Authors@R": "c(\nperson(\n\"Quirin\", \"Stier\",\nemail = \"Quirin_Stier@gmx.de\",\nrole = c(\"aut\", \"rev\", \"ctb\"),\ncomment = c(ORCID = \"0000-0002-7896-4737\")\n),\nperson(\n\"Michael\", \"Thrun\",\nemail = \"m.thrun@gmx.net\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9542-5543\")\n),\nperson(\n\"Luca\", \"Brinkmann\",\nrole = \"ctb\"\n)\n)",
  "Maintainer": "Michael Thrun <m.thrun@gmx.net>",
  "Description": "Provides a unified interface for computing, comparing, and\nexamining distances, dissimilarities, divergences, and selected\nsimilarities for bioinformatics data. The core installation\nexposes 60 canonical named routes for numerical data and more\nthan 90 when the suggested 'philentropy' backend is installed;\naliases, user-defined functions, and mixed-data combinations\nare not included in these counts. Weighted Minkowski distances\ncan be computed through 'parallelDist', an internal multicore\nimplementation, or optional 'OpenCL' kernels, while the\nestablished weighted Euclidean GPU implementation is retained\nas the optimized p = 2 route. The package also supports\ntheory-guided comparison of distance distributions for\nclustering, explicit mathematical property classifications,\nmixed-data constructions through 'manydist', and a specialized\nGene Ontology-derived TF-IDF distance.",
  "NeedsCompilation": "yes",
  "SystemRequirements": "GNU make, OpenCL library (optional, for GPU\nacceleration), pandoc (>=1.12.3, needed for vignettes)",
  "License": "GPL-3",
  "LazyLoad": "yes",
  "LazyData": "TRUE",
  "Encoding": "UTF-8",
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  "Config/testthat/edition": "3",
  "BugReports": "https://github.com/Mthrun/BIDistances/issues",
  "Config/pak/sysreqs": "cmake make libicu-dev ocl-icd-opencl-dev",
  "Repository": "https://mthrun.r-universe.dev",
  "Date/Publication": "2026-08-20 04:48:15 UTC",
  "RemoteUrl": "https://github.com/mthrun/bidistances",
  "RemoteRef": "HEAD",
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  "Packaged": {
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  "Author": "Quirin Stier [aut, rev, ctb] (ORCID:\n<https://orcid.org/0000-0002-7896-4737>),\nMichael Thrun [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-9542-5543>),\nLuca Brinkmann [ctb]",
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  "_created": "2026-09-19T08:36:46.000Z",
  "_published": "2026-09-19T08:43:12.931Z",
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    "DTW_Distance",
    "EndresSchindelin_Distance",
    "endresSchindelin_Distribution",
    "endresSchindelin_TwoSamplePDF",
    "EuclideanGPU_Distance",
    "EuclideanMulticore_Distance",
    "fast_dip_stat",
    "fast_dip_test",
    "fast_dip_test_matrix",
    "Fractional_Distance",
    "Gini_Distance",
    "Hellinger_Distance",
    "IntraClusterBoundaryCounts",
    "Jaccard_Distance",
    "KNearestNeighborsFromDistance",
    "Kullback_Leibler_div",
    "Mahalanobis_Distance",
    "Minkowski_Distance",
    "MSMD_Distance",
    "NearestNeighborIndex",
    "SharedNeighbor_Distance",
    "Tfidf_dist",
    "Tfidf_Distance",
    "ToroidalEuclidean_Distance",
    "ToroidalEuclideanDistanceOneToAll",
    "TransformSimilarity2MetricDistance",
    "TWED_Distance",
    "VariablePrecision",
    "Wasserstein_Distance"
  ],
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      "title": "Hearingloss data",
      "object": "Hearingloss_N109",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "Hepta",
      "title": "Hepta introduced in [Ultsch, 2003]",
      "object": "Hepta",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "calculateMemoryDemandGPU",
      "title": "Plan Device-Memory Batches for Weighted Euclidean Distances",
      "topics": [
        "calculateMemoryDemandGPU"
      ]
    },
    {
      "page": "Cosine_Distance",
      "title": "Cosine Distance Between Rows",
      "concept": [
        "cosine",
        "Cosine Distance",
        "Cosinus Distance",
        "Bioinformatic Distance"
      ],
      "topics": [
        "Cosine_Distance"
      ]
    },
    {
      "page": "distance-conventions",
      "title": "Distance, Metric, and Dissimilarity Conventions in BIDistances",
      "topics": [
        "distance-conventions"
      ]
    },
    {
      "page": "DistanceDistributionAnalysis",
      "title": "Analyze and Select Distance Distributions",
      "topics": [
        "DistanceDistributionAnalysis"
      ]
    },
    {
      "page": "DistanceDistributions",
      "title": "Calculate and Select Informative Distance Distributions",
      "topics": [
        "DistanceDistributions"
      ]
    },
    {
      "page": "DistanceMatrix",
      "title": "Pairwise Distance, Dissimilarity, or Similarity Matrix",
      "topics": [
        "DistanceMatrix"
      ]
    },
    {
      "page": "DistanceOneToAll",
      "title": "Distances from One Point to All Rows of a Matrix",
      "concept": [
        "Nearest neighbours"
      ],
      "topics": [
        "DistanceOneToAll"
      ]
    },
    {
      "page": "DistanceProperties",
      "title": "Mathematical Property Registry for BIDistances Methods",
      "topics": [
        "DistanceProperties"
      ]
    },
    {
      "page": "DTW_Distance",
      "title": "Dynamic Time Warping Distance",
      "concept": [
        "time series"
      ],
      "topics": [
        "DTW_Distance"
      ]
    },
    {
      "page": "EndresSchindelin_Distance",
      "title": "Endres-Schindelin Distance Matrix Between Variables",
      "concept": [
        "probability distributions"
      ],
      "topics": [
        "EndresSchindelin_Distance"
      ]
    },
    {
      "page": "endresSchindelin_Distribution",
      "title": "Endres-Schindelin Distance Between Probability Mass Vectors",
      "concept": [
        "probability distributions"
      ],
      "topics": [
        "endresSchindelin_Distribution"
      ]
    },
    {
      "page": "endresSchindelin_TwoSamplePDF",
      "title": "Endres-Schindelin Distance Between Two Samples",
      "concept": [
        "probability distributions"
      ],
      "topics": [
        "endresSchindelin_TwoSamplePDF"
      ]
    },
    {
      "page": "EuclideanGPU_Distance",
      "title": "Weighted Euclidean Distances with an Optional OpenCL Backend",
      "concept": [
        "OpenCL",
        "GPU computing",
        "high-performance computing"
      ],
      "topics": [
        "EuclideanGPU_Distance"
      ]
    },
    {
      "page": "EuclideanMulticore_Distance",
      "title": "Fast Pairwise Euclidean Distance Matrix",
      "topics": [
        "EuclideanMulticore_Distance"
      ]
    },
    {
      "page": "fast_dip_stat",
      "title": "Fast Hartigan Dip Statistic",
      "topics": [
        "fast_dip_stat"
      ]
    },
    {
      "page": "fast_dip_test",
      "title": "Fast Hartigan Dip Test for Large Samples",
      "topics": [
        "dip.test.fast",
        "dip_test_fast",
        "fast_dip_test"
      ]
    },
    {
      "page": "fast_dip_test_matrix",
      "title": "Fast Hartigan Dip Tests for Matrix Columns",
      "topics": [
        "fast_dip_test_matrix"
      ]
    },
    {
      "page": "Fractional_Distance",
      "title": "Fractional Minkowski Pairwise Distances",
      "concept": [
        "Fractional Distance",
        "Bioinformatic Distance"
      ],
      "topics": [
        "Fractional_Distance"
      ]
    },
    {
      "page": "Gini_Distance",
      "title": "Gini Distance Between Cases",
      "concept": [
        "Gini coefficient"
      ],
      "topics": [
        "Gini_Distance"
      ]
    },
    {
      "page": "Hearingloss_N109",
      "title": "Hearingloss data",
      "topics": [
        "Hearingloss_N109"
      ]
    },
    {
      "page": "Hellinger_Distance",
      "title": "Hellinger Distance Between Binary-Class Conditional Densities",
      "topics": [
        "Hellinger_Distance"
      ]
    },
    {
      "page": "Hepta",
      "title": "Hepta introduced in [Ultsch, 2003]",
      "topics": [
        "Hepta"
      ]
    },
    {
      "page": "IntraClusterBoundaryCounts",
      "title": "Count Intra-Cluster Distances Above a Boundary",
      "topics": [
        "IntraClusterBoundaryCounts"
      ]
    },
    {
      "page": "Jaccard_Distance",
      "title": "Generalized Jaccard Distance Between Cases",
      "topics": [
        "Jaccard_Distance"
      ]
    },
    {
      "page": "KNearestNeighborsFromDistance",
      "title": "Find Nearest Neighbors from a Distance Matrix",
      "topics": [
        "KNearestNeighborsFromDistance"
      ]
    },
    {
      "page": "Kullback_Leibler_div",
      "title": "Smoothed Kullback-Leibler Divergence",
      "concept": [
        "Kullback-Leibler divergence"
      ],
      "topics": [
        "Kullback_Leibler_div"
      ]
    },
    {
      "page": "Mahalanobis_Distance",
      "title": "Pairwise Squared Generalized Mahalanobis Distances",
      "topics": [
        "Mahalanobis_Distance"
      ]
    },
    {
      "page": "Minkowski_Distance",
      "title": "Weighted Minkowski Distances with Selectable CPU and OpenCL Backends",
      "concept": [
        "Minkowski distance",
        "OpenCL",
        "GPU computing",
        "high-performance computing"
      ],
      "topics": [
        "Minkowski_Distance"
      ]
    },
    {
      "page": "MSMD_Distance",
      "title": "Move-Split-Merge Distance",
      "concept": [
        "time series"
      ],
      "topics": [
        "MSMD_Distance"
      ]
    },
    {
      "page": "NearestNeighborIndex",
      "title": "Index of the Nearest Other Row",
      "topics": [
        "NearestNeighborIndex"
      ]
    },
    {
      "page": "SharedNeighbor_Distance",
      "title": "Shared-Nearest-Neighbor Distance",
      "concept": [
        "Shared Neighbor Distance",
        "Bioinformatic Distance"
      ],
      "topics": [
        "SharedNeighbor_Distance"
      ]
    },
    {
      "page": "Tfidf_Distance",
      "title": "TF-IDF-Derived Distance Between Genes",
      "concept": [
        "TF-IDF",
        "Bioinformatic distance",
        "Term frequency",
        "Inverse document frequency"
      ],
      "topics": [
        "Tfidf_dist",
        "Tfidf_Distance"
      ]
    },
    {
      "page": "ToroidalEuclidean_Distance",
      "title": "Pairwise Toroidal Euclidean Distances",
      "topics": [
        "ToroidalEuclidean_Distance"
      ]
    },
    {
      "page": "ToroidalEuclideanDistanceOneToAll",
      "title": "Toroidal Euclidean Distances from One Position",
      "topics": [
        "ToroidalEuclideanDistanceOneToAll"
      ]
    },
    {
      "page": "TransformSimilarity2MetricDistance",
      "title": "Transform a Similarity Matrix to a Metric Distance Matrix",
      "concept": [
        "similarity"
      ],
      "topics": [
        "TransformSimilarity2MetricDistance"
      ]
    },
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      "title": "Time Warp Edit Distance",
      "concept": [
        "time series"
      ],
      "topics": [
        "TWED_Distance"
      ]
    },
    {
      "page": "VariablePrecision",
      "title": "Smallest Within-Variable Differences",
      "topics": [
        "VariablePrecision"
      ]
    },
    {
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      "title": "Wasserstein Distance Between Cases",
      "concept": [
        "optimal transport",
        "wasserstein distance"
      ],
      "topics": [
        "Wasserstein_Distance"
      ]
    }
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        "OpenCL route",
        "Gene Ontology-derived TF-IDF distances",
        "Time-series and mixed-data routes",
        "Which entry point should be used?",
        "References"
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