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  "Package": "depower",
  "Title": "Power Analysis for Differential Expression Studies",
  "Version": "2026.11.11",
  "Authors@R": "c(\nperson(\ngiven = \"Brett\",\nfamily = \"Klamer\",\nemail = \"code@brettklamer.com\",\ncomment = c(ORCID = \"0000-0002-8334-4831\"),\nrole = c(\"aut\", \"cre\")\n),\nperson(\ngiven = \"Lianbo\",\nfamily = \"Yu\",\nemail = \"lianbo.yu@osumc.edu\",\ncomment = c(ORCID = \"0000-0002-2025-2585\"),\nrole = c(\"aut\")\n)\n)",
  "Description": "Provides a convenient framework to simulate data, run\nhypothesis tests, estimate power, and plot the results for\ndifferential expression studies. The simulation-based framework\nis described in Klamer and Yu (2026)\n<doi:10.1177/11769351261431247>. Outcomes may be lognormal,\nnegative binomial, or bivariate negative binomial. Supported\ndesigns are one-sample, independent two-sample, and dependent\ntwo-sample comparisons. Built-in tests include t-tests, Wald\ntests, and likelihood ratio tests. User-defined tests are also\naccepted. The significance level can be set to control the\nper-family error rate or the family-wise error rate. Negative\nbinomial methods are described in Yu, Fernandez, and Brock\n(2017) <doi:10.1186/s12859-017-1648-2> and Yu, Fernandez, and\nBrock (2020) <doi:10.1186/s12859-020-3541-7>.",
  "URL": "https://brettklamer.com/work/depower/,\nhttps://bitbucket.org/bklamer/depower/",
  "License": "MIT + file LICENSE",
  "RdMacros": "Rdpack",
  "Language": "en-US",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
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  "Date/Publication": "2026-11-11 18:05:19 UTC",
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    "User": "root"
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  "Author": "Brett Klamer [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-8334-4831>),\nLianbo Yu [aut] (ORCID: <https://orcid.org/0000-0002-2025-2585>)",
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  "_type": "src",
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  "_created": "2026-11-11T18:55:41.000Z",
  "_published": "2026-11-11T19:02:33.253Z",
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      "title": "Add Bayesian posterior predictive intervals for power estimates",
      "topics": [
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      "title": "Test statistic distribution under the null",
      "topics": [
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        "distribution",
        "simulated"
      ]
    },
    {
      "page": "eval_power_ci",
      "title": "Evaluate confidence intervals for power estimates",
      "topics": [
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      ]
    },
    {
      "page": "eval_power_pi",
      "title": "Evaluate Bayesian posterior predictive intervals for power estimates",
      "topics": [
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      ]
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      "page": "glm_nb",
      "title": "GLM for NB ratio of means",
      "topics": [
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    {
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      "title": "GLMM for BNB ratio of means",
      "topics": [
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      "title": "Likelihood ratio test for NB ratio of means",
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        "mle_bnb_null"
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      "title": "MLE for NB",
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        "mle_nb_alt",
        "mle_nb_null"
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