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  "Title": "A Bayesian Hierarchical Model that Controls for Non-Adherence in\nMobile Menstrual Cycle Tracking",
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  "Authors@R": "person(\"Luke\", \"Duttweiler\", , \"lduttweiler@hsph.harvard.edu\", role = c(\"aut\", \"cre\", 'cph'),\ncomment = c(ORCID = \"0000-0002-0467-995X\"))",
  "Description": "Implements a Bayesian hierarchical model designed to\nidentify skips in mobile menstrual cycle self-tracking on\nmobile apps. Future developments will allow for the inclusion\nof covariates affecting cycle mean and regularity, as well as\nextra information regarding tracking non-adherence. Main\nmethods to be outlined in a forthcoming paper, with alternative\nmodels from Li et al. (2022) <doi:10.1093/jamia/ocab182>.",
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  "Repository": "https://lukeduttweiler.r-universe.dev",
  "Date/Publication": "2026-04-30 17:47:07 UTC",
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      "title": "Simulate user tracked menstrual cycle data for an individual using the li model.",
      "topics": [
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      "title": "Simulate user tracked menstrual cycle data for an individual using the li model.",
      "topics": [
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      "title": "Simulate user tracked menstrual cycle data for an individual using the mixture model.",
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      "page": "mixSimCAT",
      "title": "Simulate user tracked menstrual cycle data for an individual using the mixture model (categorical covariates).",
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      "title": "Plot skipTrack.model objects",
      "topics": [
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      "title": "Sample a value from the full conditional posterior of b_i",
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