{ "_id": "6a12b791acfb0bcc41d1b12d", "Package": "InfluenceBorrowing", "Type": "Package", "Title": "Adaptive Influence-Based Borrowing for Hybrid Control Trials", "Version": "0.1.0", "Authors@R": "c(person(given = \"Jile\", family = \"Chaoge\", role = c(\"aut\", \"cre\"), \nemail = \"chogjill@126.com\"),\nperson(given = \"Peng\", family = \"Wu\", role = \"aut\"),\nperson(given = \"Shu\", family = \"Yang\", role = \"aut\"))", "Description": "Implements the adaptive influence-based borrowing\nframework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu\nYang (2026+) in the paper ``Improving Treatment Effect\nEstimation in Trials through Adaptive Borrowing of External\nControls\" for augmenting\nRandomized Controlled Trials (RCTs) with External Control (EC)\ndata. This package provides a comprehensive workflow to: (1)\nquantify the comparability of external control samples using\ninfluence scores approximated via the influence function of the\nM-estimator; (2) construct candidate borrowing subsets and\nselect the optimal subset that minimizes the Mean Squared Error\n(MSE); and (3) calibrate systematic differences in external\noutcomes using R-learner methods implemented via Ordinary Least\nSquares or Kernel Ridge Regression.", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-24 08:29:43 UTC", "User": "root" }, "Author": "Jile Chaoge [aut, cre], Peng Wu [aut], Shu Yang [aut]", "Maintainer": "Jile Chaoge ", "Repository": "https://jilechaoge.r-universe.dev", "Date/Publication": "2026-04-23 22:05:07 UTC", "RemoteUrl": "https://github.com/cran/InfluenceBorrowing", "RemoteRef": "HEAD", "RemoteSha": "7edef2b74333966799ea405ff1bcb6f153b62245", "MD5sum": 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