{
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  "Title": "Time-Smoothed Inverse Probability Weighting for Repeatedly\nMeasured Outcomes",
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  "Authors@R": "c(person(\"Sean\", \"McGrath\",\nrole = c(\"aut\", \"cre\"),\nemail = \"sean.mcgrath514@gmail.com\",\ncomment = c(ORCID = \"0000-0002-7281-3516\")),\nperson(\"Takuya\", \"Kawahara\",\nrole = c(\"aut\")),\nperson(\"Jessica\", \"Young\",\nrole = c(\"aut\"),\nemail = \"jyoung@hsph.harvard.edu\",\ncomment = c(ORCID = \"0000-0002-2758-6932\")))",
  "Description": "Implements several methods to estimate effects of\ngeneralized time-varying treatment strategies on the mean of an\noutcome at one or more selected follow-up times of interest.\nSpecifically, the package implements the time-smoothed inverse\nprobability weighted estimators described in McGrath et al.\n(2025) <doi:10.48550/arXiv.2509.13971>. Outcomes may be\nrepeatedly, non-monotonically, informatively, and sparsely\nmeasured in the data source. The package also supports settings\nwhere outcomes are truncated by death, i.e. some individuals\ndie during follow-up which renders the outcome of interest\nundefined at the follow-up time of interest.",
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  "Repository": "https://stmcg.r-universe.dev",
  "Date/Publication": "2026-06-03 01:54:46 UTC",
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