{
  "_id": "6a1ff4a9b401979e73442ec8",
  "Package": "dependentsimr",
  "Title": "Simulate Omics-Scale Data with Dependency",
  "Version": "1.0.0.0",
  "Authors@R": "person(\"Thomas\", \"Brooks\", , \"tgbrooks@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-6980-0079\"))",
  "Description": "Using a Gaussian copula approach, this package generates\nsimulated data mimicking a target real dataset. It supports\nnormal, Poisson, empirical, and 'DESeq2' (negative binomial\nwith size factors) marginal distributions. It uses an low-rank\nplus diagonal covariance matrix to efficiently generate\nomics-scale data. Methods are described in: Yang, Grant, and\nBrooks (2025) <doi:10.1101/2025.01.31.634335>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
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  "Config/testthat/edition": "3",
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  "Repository": "https://tgbrooks.r-universe.dev",
  "Date/Publication": "2025-10-06 18:06:45 UTC",
  "RemoteUrl": "https://github.com/tgbrooks/dependentsimr",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-03 09:26:49 UTC",
    "User": "root"
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  "Author": "Thomas Brooks [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0002-6980-0079>)",
  "Maintainer": "Thomas Brooks <tgbrooks@gmail.com>",
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    "email": "tgbrooks@gmail.com",
    "login": "tgbrooks",
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    "description": "Bioinformatician at UPenn ITMAT Bioinformatics Lab",
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    "extra/citation.html",
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      "version": "1.0.0.0",
      "date": "2025-07-23"
    }
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  "_exports": [
    "draw_from_multivariate_corr",
    "get_random_structure",
    "match_with_spiked_wishart",
    "multi_sample_spiked_wishart",
    "remove_dependence",
    "sample_spiked_wishart",
    "sample_spiked_wishart_and_jac"
  ],
  "_datasets": [
    {
      "name": "read_counts",
      "title": "GSE151923: cortex from 6-month-old wildtype C57BL/6 mice",
      "object": "read_counts",
      "class": [
        "spec_tbl_df",
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "gene_id",
        "Arpp19_10_3",
        "Arpp19_4_1",
        "Arpp19_5_1",
        "Arpp19_5_3",
        "Arpp19_5_4",
        "Arpp19_6_1",
        "Arpp19_7_2",
        "Arpp19_9_1",
        "Dach1_11_1",
        "Dach1_2_2",
        "Dach1_3_1",
        "Dach1_5_3"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "draw_from_multivariate_corr",
      "title": "Draw random samples from the given random structure",
      "topics": [
        "draw_from_multivariate_corr"
      ]
    },
    {
      "page": "get_random_structure",
      "title": "Compute structure of dependency from a given data",
      "topics": [
        "get_random_structure"
      ]
    },
    {
      "page": "match_with_spiked_wishart",
      "title": "Compute what spiked SD values will give you the desired top eigenvalues by iteratively solving",
      "topics": [
        "match_with_spiked_wishart"
      ]
    },
    {
      "page": "multi_sample_spiked_wishart",
      "title": "Compute means of each singular value and the mean Jacobian, see sample_spiked_wishart_and_jac",
      "topics": [
        "multi_sample_spiked_wishart"
      ]
    },
    {
      "page": "read_counts",
      "title": "GSE151923: cortex from 6-month-old wildtype C57BL/6 mice",
      "topics": [
        "read_counts"
      ]
    },
    {
      "page": "remove_dependence",
      "title": "Remove all dependence in a random structure",
      "topics": [
        "remove_dependence"
      ]
    },
    {
      "page": "sample_spiked_wishart",
      "title": "Efficiently sample the singular values corresponding to a random Wishart matrix with spiked eigenvalues Specifically, if W = G G^T with each column of G drawn iid from N(0, Sigma), then W is a Wishart matrix and this function samples the singular values of G. The eigenvalues of W are just the squares of the singular values. Here, Sigma is diagonal with its leading entries from spiked_sd^2 and all remaining entries are population_sd^2.",
      "topics": [
        "sample_spiked_wishart"
      ]
    },
    {
      "page": "sample_spiked_wishart_and_jac",
      "title": "Efficiently sample the singular values corresponding to a random Wishart matrix with spiked eigenvalues and the Jacobian I.e., these are the singular values of G if GG^T is Wishart. The square of these give the eigenvalues of the random Wishart matrix.",
      "topics": [
        "sample_spiked_wishart_and_jac"
      ]
    }
  ],
  "_readme": "https://github.com/tgbrooks/dependentsimr/raw/HEAD/README.md",
  "_rundeps": [
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  ],
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      "title": "Simulating data with dependentsimr",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Setup",
        "Load data",
        "Get Dependence Structure",
        "Generate Data",
        "Simulate Control Data",
        "Simulate Differentially Expressed Data",
        "Plot the generated data"
      ],
      "created": "2025-01-28 14:31:13",
      "modified": "2025-07-11 17:25:47",
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