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  "Package": "iPEB",
  "Type": "Package",
  "Title": "Improved Parametric Empirical Bayes for Longitudinal Biomarker\nAnalysis",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Bitan\", \"Sarkar\", email = \"bitansarkar010899@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0009-0000-5178-5934\")),\nperson(c(\"Ana\", \"Maria\"), \"Kenney\", role = \"aut\"),\nperson(c(\"James\", \"P.\"), \"Long\", role = \"aut\"),\nperson(c(\"Johannes\", \"F.\"), \"Fahrmann\", role = \"aut\"),\nperson(\"Samir\", \"Hanash\", role = \"aut\"),\nperson(\"Kim-Anh\", \"Do\", role = \"aut\"),\nperson(\"Ehsan\", \"Irajizad\", role = \"aut\"))",
  "Description": "Extends parametric empirical Bayes (PEB) for longitudinal\nbiomarker screening with a time-gap-aware standardization\nlayer, covariate adjustment, and objective-driven multi-marker\nweighting. The layer models each subject's biomarker history\nwith a random intercept (and an optional random slope) and\nautocorrelated, gap-scaled residuals, so that prediction\nuncertainty grows with the time between visits and per-visit\nspecificity is preserved under irregular sampling. Marker\nweights are learned to optimize a user-selected clinical\nobjective -- maximizing sensitivity at a fixed specificity,\nextending detection lead time, or a combined objective -- with\noptional feature selection and a choice of scalar or\nmultivariate combiner. Functions for fitting, prediction, and\nevaluation (sensitivity, lead time, and specificity at chosen\noperating points) are provided. A manuscript describing the\nmethod is in preparation.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "VignetteBuilder": "knitr",
  "URL": "https://github.com/bitansa/iPEB",
  "BugReports": "https://github.com/bitansa/iPEB/issues",
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  "Repository": "https://bitansa.r-universe.dev",
  "Date/Publication": "2026-09-24 01:59:32 UTC",
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    "User": "root"
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  "_published": "2026-09-24T05:30:10.862Z",
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