{
  "_id": "6a102328acfb0bcc41c8d49c",
  "Package": "MIMSunit",
  "Type": "Package",
  "Title": "Algorithm to Compute Monitor Independent Movement Summary Unit\n(MIMS-Unit)",
  "Version": "0.11.3",
  "Date": "2026-05-13",
  "Authors@R": "c(person(\"Qu\", \"Tang\", email = \"tang.q@northeastern.edu\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0001-5415-0205\")),\nperson(\"Dinesh\", \"John\", email = \"d.john@northeastern.edu\",\nrole = c(\"aut\")),\nperson(\"Stephen\", \"Intille\", email = \"s.intille@northeastern.edu\",\nrole = c(\"aut\")),\nperson(\"Umberto\", \"Mazzucchelli\", email = \"mazzucchelli.u@northeastern.edu\",\nrole = c(\"cre\"), comment = c(ORCID = \"0009-0000-1071-6027\")),\nperson(\"mHealth Research Group\", role = c(\"cph\"),\ncomment = \"https://www.mhealthgroup.org\")\n)",
  "Description": "The MIMS-unit algorithm is developed to compute Monitor\nIndependent Movement Summary Unit, a measurement to summarize\nraw accelerometer data while ensuring harmonized results across\ndifferent devices. It also includes scripts to reproduce\nresults in the related publication (John, D., Tang. Q.,\nAlbinali, F. and Intille, S. (2019)\n<doi:10.1123/jmpb.2018-0068>).",
  "License": "MIT + file LICENSE",
  "Language": "en-US",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "RoxygenNote": "7.2.0",
  "URL": "https://mhealthgroup.github.io/MIMSunit/,\nhttps://github.com/mhealthgroup/MIMSunit/tree/master",
  "BugReports": "https://github.com/mhealthgroup/MIMSunit/issues/",
  "SystemRequirements": "memory (>= 4GB) Ubuntu: build-essential,\nlibxml2-dev, libssl-dev, libcurl4-openssl-dev Windows: Rtools\n(>= 3.5)",
  "Repository": "https://mhealthgroup.r-universe.dev",
  "Date/Publication": "2026-05-13 20:58:55 UTC",
  "RemoteUrl": "https://github.com/mhealthgroup/mimsunit",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-13 21:58:47 UTC",
    "User": "root"
  },
  "Author": "Qu Tang [aut] (ORCID: <https://orcid.org/0000-0001-5415-0205>),\nDinesh John [aut],\nStephen Intille [aut],\nUmberto Mazzucchelli [cre] (ORCID:\n<https://orcid.org/0009-0000-1071-6027>),\nmHealth Research Group [cph] (https://www.mhealthgroup.org)",
  "Maintainer": "Umberto Mazzucchelli <mazzucchelli.u@northeastern.edu>",
  "MD5sum": "784ddd1fb34fbeed198ee381fc41c1c6",
  "_user": "mhealthgroup",
  "_type": "src",
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  "_created": "2026-05-13T21:58:47.000Z",
  "_published": "2026-05-22T09:34:32.584Z",
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    "message": "Address CRAN pretest notes\n",
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  "_selfowned": true,
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  "_updates": [
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    "name": "mHealthGroup"
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  "_downloads": {
    "count": 59,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/MIMSunit"
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  "_pkgdown": "https://mhealthgroup.github.io/MIMSunit/",
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  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/MIMSunit.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
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    "extra/readme.md",
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  "_homeurl": "https://github.com/mhealthgroup/mimsunit",
  "_realowner": "mhealthgroup",
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  "_releases": [
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      "date": "2020-03-02"
    },
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      "date": "2020-04-25"
    },
    {
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    {
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  "_exports": [
    "aggregate_for_mims",
    "aggregate_for_orientation",
    "bandlimited_interp",
    "clip_data",
    "compute_orientation",
    "custom_mims_unit",
    "cut_off_signal",
    "export_to_actilife",
    "extrapolate",
    "extrapolate_rate",
    "extrapolate_single_col",
    "generate_interactive_plot",
    "iir",
    "illustrate_extrapolation",
    "illustrate_signal",
    "import_actigraph_count_csv",
    "import_actigraph_csv",
    "import_actigraph_csv_chunked",
    "import_actigraph_meta",
    "import_activpal3_csv",
    "import_enmo_csv",
    "import_mhealth_csv",
    "import_mhealth_csv_chunked",
    "interpolate_signal",
    "mims_unit",
    "mims_unit_from_files",
    "parse_epoch_string",
    "sampling_rate",
    "segment_data",
    "sensor_orientations",
    "shiny_app",
    "simulate_new_data",
    "sum_up",
    "vector_magnitude"
  ],
  "_datasets": [
    {
      "name": "conceptual_diagram_data",
      "title": "The input accelerometer data used to generate the conceptual diagram (Figure 1) in the manuscript.",
      "object": "conceptual_diagram_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "HEADER_TIME_STAMP",
        "X",
        "SR",
        "GRANGE",
        "NAME"
      ],
      "rows": 1704,
      "table": true,
      "tojson": true
    },
    {
      "name": "cv_different_algorithms",
      "title": "Coefficient of variation values for different acceleration data summary algorithms",
      "object": "cv_different_algorithms",
      "class": [
        "data.frame"
      ],
      "fields": [
        "TYPE",
        "HZ",
        "COEFF_OF_VARIATION"
      ],
      "rows": 30,
      "table": true,
      "tojson": true
    },
    {
      "name": "edge_case",
      "title": "A short snippet of raw accelerometer signal from a device that has ending data maxed out.",
      "object": "edge_case",
      "class": [
        "data.frame"
      ],
      "fields": [
        "HEADER_TIME_STAMP",
        "X",
        "Y",
        "Z"
      ],
      "rows": 20001,
      "table": true,
      "tojson": true
    },
    {
      "name": "measurements_different_devices",
      "title": "The mean and standard deviation of accelerometer summary measure for different acceleration data summary algorithms and for different devices.",
      "object": "measurements_different_devices",
      "class": [
        "data.frame"
      ],
      "fields": [
        "DEVICE",
        "GRANGE",
        "SR",
        "TYPE",
        "HZ",
        "NAME",
        "mean",
        "sd"
      ],
      "rows": 235,
      "table": true,
      "tojson": true
    },
    {
      "name": "rest_on_table",
      "title": "A short snippet of raw accelerometer signal from a device resting on a table.",
      "object": "rest_on_table",
      "class": [
        "data.frame"
      ],
      "fields": [
        "HEADER_TIME_STAMP",
        "X",
        "Y",
        "Z"
      ],
      "rows": 4999,
      "table": true,
      "tojson": true
    },
    {
      "name": "sample_raw_accel_data",
      "title": "Sample raw accelerometer data",
      "object": "sample_raw_accel_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "HEADER_TIME_STAMP",
        "X",
        "Y",
        "Z"
      ],
      "rows": 480,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "aggregate_for_mims",
      "title": "Aggregate over epoch to get numerically integrated values.",
      "concept": [
        "aggregate functions"
      ],
      "topics": [
        "aggregate_for_mims"
      ]
    },
    {
      "page": "aggregate_for_orientation",
      "title": "Aggregate over epoch to get estimated accelerometer orientation.",
      "concept": [
        "aggregate functions"
      ],
      "topics": [
        "aggregate_for_orientation"
      ]
    },
    {
      "page": "bandlimited_interp",
      "title": "Apply a bandlimited interpolation filter to the signal to change the sampling rate",
      "concept": [
        "filtering functions"
      ],
      "topics": [
        "bandlimited_interp"
      ]
    },
    {
      "page": "clip_data",
      "title": "Clip dataframe to the given start and stop time",
      "concept": [
        "utility functions"
      ],
      "topics": [
        "clip_data"
      ]
    },
    {
      "page": "compute_orientation",
      "title": "Estimate the accelerometer orientation",
      "concept": [
        "transformation functions"
      ],
      "topics": [
        "compute_orientation"
      ]
    },
    {
      "page": "conceptual_diagram_data",
      "title": "The input accelerometer data used to generate the conceptual diagram (Figure 1) in the manuscript.",
      "topics": [
        "conceptual_diagram_data"
      ]
    },
    {
      "page": "custom_mims_unit",
      "title": "Compute both MIMS-unit and sensor orientations with custom settings",
      "concept": [
        "Top level API functions"
      ],
      "topics": [
        "custom_mims_unit"
      ]
    },
    {
      "page": "cut_off_signal",
      "title": "Cut off input multi-channel signal according to a new dynamic range",
      "concept": [
        "utility functions"
      ],
      "topics": [
        "cut_off_signal"
      ]
    },
    {
      "page": "cv_different_algorithms",
      "title": "Coefficient of variation values for different acceleration data summary algorithms",
      "topics": [
        "cv_different_algorithms"
      ]
    },
    {
      "page": "edge_case",
      "title": "A short snippet of raw accelerometer signal from a device that has ending data maxed out.",
      "topics": [
        "edge_case"
      ]
    },
    {
      "page": "export_to_actilife",
      "title": "Export accelerometer data in Actilife RAW CSV format",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "export_to_actilife"
      ]
    },
    {
      "page": "extrapolate",
      "title": "Extrapolate input multi-channel accelerometer data",
      "concept": [
        "extrapolation related functions"
      ],
      "topics": [
        "extrapolate",
        "extrapolate_single_col"
      ]
    },
    {
      "page": "extrapolate_rate",
      "title": "Get extrapolation rate.",
      "concept": [
        "extrapolation related functions"
      ],
      "topics": [
        "extrapolate_rate"
      ]
    },
    {
      "page": "generate_interactive_plot",
      "title": "Plot MIMS unit values or raw signal using dygraphs interactive plotting library.",
      "concept": [
        "visualization functions."
      ],
      "topics": [
        "generate_interactive_plot"
      ]
    },
    {
      "page": "iir",
      "title": "Apply IIR filter to the signal",
      "concept": [
        "filtering functions"
      ],
      "topics": [
        "iir"
      ]
    },
    {
      "page": "illustrate_extrapolation",
      "title": "Plot illustrations about extrapolation in illustration style.",
      "concept": [
        "visualization functions."
      ],
      "topics": [
        "illustrate_extrapolation"
      ]
    },
    {
      "page": "illustrate_signal",
      "title": "Plot given raw signal in illustration diagram style.",
      "concept": [
        "visualization functions."
      ],
      "topics": [
        "illustrate_signal"
      ]
    },
    {
      "page": "import_actigraph_count_csv",
      "title": "Import Actigraph count data stored in Actigraph summary csv format",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_actigraph_count_csv"
      ]
    },
    {
      "page": "import_actigraph_csv",
      "title": "Import raw multi-channel accelerometer data stored in Actigraph raw csv format",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_actigraph_csv"
      ]
    },
    {
      "page": "import_actigraph_csv_chunked",
      "title": "Import large raw multi-channel accelerometer data stored in Actigraph raw csv format in chunks",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_actigraph_csv_chunked"
      ]
    },
    {
      "page": "import_actigraph_meta",
      "title": "Import The meta information stored in Actigraph RAW or summary csv file.",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_actigraph_meta"
      ]
    },
    {
      "page": "import_activpal3_csv",
      "title": "Import raw multi-channel accelerometer data stored in ActivPal3 csv format",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_activpal3_csv"
      ]
    },
    {
      "page": "import_enmo_csv",
      "title": "Import ENMO data stored in csv csv",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_enmo_csv"
      ]
    },
    {
      "page": "import_mhealth_csv",
      "title": "Import raw multi-channel accelerometer data stored in mHealth Specification",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_mhealth_csv"
      ]
    },
    {
      "page": "import_mhealth_csv_chunked",
      "title": "Import large raw multi-channel accelerometer data stored in mHealth Specification in chunks.",
      "concept": [
        "File I/O functions"
      ],
      "topics": [
        "import_mhealth_csv_chunked"
      ]
    },
    {
      "page": "interpolate_signal",
      "title": "Interpolate missing points and unify sampling rate for multi-channel signal",
      "concept": [
        "utility functions"
      ],
      "topics": [
        "interpolate_signal"
      ]
    },
    {
      "page": "measurements_different_devices",
      "title": "The mean and standard deviation of accelerometer summary measure for different acceleration data summary algorithms and for different devices.",
      "topics": [
        "measurements_different_devices"
      ]
    },
    {
      "page": "mims_unit",
      "title": "Compute Monitor Independent Motion Summary unit (MIMS-unit)",
      "concept": [
        "Top level API functions"
      ],
      "topics": [
        "mims_unit",
        "mims_unit_from_files"
      ]
    },
    {
      "page": "parse_epoch_string",
      "title": "Parse epoch string to the corresponding number of samples it represents.",
      "concept": [
        "utility functions"
      ],
      "topics": [
        "parse_epoch_string"
      ]
    },
    {
      "page": "rest_on_table",
      "title": "A short snippet of raw accelerometer signal from a device resting on a table.",
      "topics": [
        "rest_on_table"
      ]
    },
    {
      "page": "sample_raw_accel_data",
      "title": "Sample raw accelerometer data",
      "topics": [
        "sample_raw_accel_data"
      ]
    },
    {
      "page": "sampling_rate",
      "title": "Estimate sampling rate for multi-channel signal",
      "concept": [
        "utility functions"
      ],
      "topics": [
        "sampling_rate"
      ]
    },
    {
      "page": "segment_data",
      "title": "Segment input dataframe into windows as specified by breaks. 'segment_data' segments the input sensor dataframe into epoch windows with length specified in breaks.",
      "concept": [
        "utility functions"
      ],
      "topics": [
        "segment_data"
      ]
    },
    {
      "page": "sensor_orientations",
      "title": "Estimates sensor orientation",
      "concept": [
        "Top level API functions"
      ],
      "topics": [
        "sensor_orientations"
      ]
    },
    {
      "page": "shiny_app",
      "title": "Run shiny app to compute MIMSunit values from files",
      "concept": [
        "Top level API functions"
      ],
      "topics": [
        "shiny_app"
      ]
    },
    {
      "page": "simulate_new_data",
      "title": "Simulate new data based on the given multi-channel accelerometer data",
      "concept": [
        "utility functions"
      ],
      "topics": [
        "simulate_new_data"
      ]
    },
    {
      "page": "sum_up",
      "title": "Sum of multi-channel signal.",
      "concept": [
        "transformation functions"
      ],
      "topics": [
        "sum_up"
      ]
    },
    {
      "page": "vector_magnitude",
      "title": "Vector magnitude of multi-channel signal.",
      "concept": [
        "transformation functions"
      ],
      "topics": [
        "vector_magnitude"
      ]
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