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  "Title": "Temporal Tensor Decomposition, a Dimensionality Reduction Tool\nfor Longitudinal Multivariate Data",
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  "Description": "TEMPoral TEnsor Decomposition (TEMPTED), is a dimension\nreduction method for multivariate longitudinal data with\nvarying temporal sampling. It formats the data into a temporal\ntensor and decomposes it into a summation of low-dimensional\ncomponents, each consisting of a subject loading vector, a\nfeature loading vector, and a continuous temporal loading\nfunction. These loadings provide a low-dimensional\nrepresentation of subjects or samples and can be used to\nidentify features associated with clusters of subjects or\nsamples. TEMPTED provides the flexibility of allowing subjects\nto have different temporal sampling, so time points do not need\nto be binned, and missing time points do not need to be\nimputed.",
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  "Maintainer": "Pixu Shi <pixu.shi@duke.edu>",
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    "ratio_feature",
    "reconstruct",
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    "tdenoise",
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        "delivery",
        "day_of_life"
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      "tojson": true
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        "array"
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      "table": true,
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      "page": "aggregate_feature",
      "title": "Aggregate features using feature loadings",
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    },
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      "page": "bernoulli_kernel",
      "title": "Caculate the Bernoulli kernel",
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      ]
    },
    {
      "page": "count_table",
      "title": "OTU read count table from the ECAM data",
      "topics": [
        "count_table"
      ]
    },
    {
      "page": "est_test_subject",
      "title": "Estimate subject loading of testing data",
      "topics": [
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      "title": "Meta data table from the ECAM data",
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      "title": "Plot nonparametric smoothed mean and error bands of features versus time",
      "topics": [
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    },
    {
      "page": "plot_metafeature",
      "title": "Plot nonparametric smoothed mesan and error bands of meta features versus time",
      "topics": [
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      ]
    },
    {
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      "title": "Plot the temporal loading functions",
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    },
    {
      "page": "processed_table",
      "title": "Central-log-ratio (clr) transformed OTU table from the ECAM data",
      "topics": [
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    },
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      "title": "Calculate the de-noised temporal tensor",
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      "title": "Decomposition of temporal tensor",
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