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InfluenceBorrowing: Adaptive Influence-Based Borrowing for Hybrid Control Trials

Implements the adaptive influence-based borrowing framework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper “Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls" <doi:10.48550/arXiv.2604.13973> for augmenting Randomized Controlled Trials (RCTs) with External Control (EC) data. This package provides a comprehensive workflow to: (1) quantify the comparability of external control samples using influence scores approximated via the influence function of the M-estimator; (2) construct candidate borrowing subsets and select the optimal subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic differences in external outcomes using R-learner methods implemented via Ordinary Least Squares or Kernel Ridge Regression.

Version: 0.1.0
Imports: KRLS, stats
Published: 2026-04-23
DOI: 10.32614/CRAN.package.InfluenceBorrowing
Author: Jile Chaoge [aut, cre], Peng Wu [aut], Shu Yang [aut]
Maintainer: Jile Chaoge <chogjill at 126.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: InfluenceBorrowing results

Documentation:

Reference manual: InfluenceBorrowing.html , InfluenceBorrowing.pdf

Downloads:

Package source: InfluenceBorrowing_0.1.0.tar.gz
Windows binaries: r-devel: InfluenceBorrowing_0.1.0.zip, r-release: InfluenceBorrowing_0.1.0.zip, r-oldrel: InfluenceBorrowing_0.1.0.zip
macOS binaries: r-release (arm64): InfluenceBorrowing_0.1.0.tgz, r-oldrel (arm64): InfluenceBorrowing_0.1.0.tgz, r-release (x86_64): InfluenceBorrowing_0.1.0.tgz, r-oldrel (x86_64): InfluenceBorrowing_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=InfluenceBorrowing to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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