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"), 
                    email = "chogjill@126.com"), 
             person(given = "Peng", family = "Wu", role = "aut"),
             person(given = "Shu", family = "Yang", role = "aut"))
Description: 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. 
License: GPL-3
Encoding: UTF-8
Imports: KRLS, stats
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-04-22 15:07:59 UTC; 14425
Author: Jile Chaoge [aut, cre],
  Peng Wu [aut],
  Shu Yang [aut]
Maintainer: Jile Chaoge <chogjill@126.com>
Repository: CRAN
Date/Publication: 2026-04-23 20:20:13 UTC
Built: R 4.6.0; ; 2026-04-27 10:11:35 UTC; unix
