Package: InfluenceBorrowing 0.1.0
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.
Authors:
InfluenceBorrowing_0.1.0.tar.gz
InfluenceBorrowing_0.1.0.zip(r-4.7)InfluenceBorrowing_0.1.0.zip(r-4.6)InfluenceBorrowing_0.1.0.zip(r-4.5)
InfluenceBorrowing_0.1.0.tgz(r-4.6-any)InfluenceBorrowing_0.1.0.tgz(r-4.5-any)
InfluenceBorrowing_0.1.0.tar.gz(r-4.7-any)InfluenceBorrowing_0.1.0.tar.gz(r-4.6-any)
InfluenceBorrowing_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
InfluenceBorrowing/json (API)
| # Install 'InfluenceBorrowing' in R: |
| install.packages('InfluenceBorrowing', repos = c('https://jilechaoge.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:7edef2b743. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 104 | ||
| source / vignettes | OK | 195 | ||
| linux-release-x86_64 | OK | 100 | ||
| macos-release-arm64 | OK | 113 | ||
| macos-oldrel-arm64 | OK | 118 | ||
| windows-devel | OK | 66 | ||
| windows-release | OK | 71 | ||
| windows-oldrel | OK | 57 | ||
| wasm-release | OK | 85 |
Exports:compute_influencesestimate_rctestimate_selectedfind_optimal_kgen_demo_datarlearner_krlsrlearner_lm
Dependencies:KRLS
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Calculate Influence Scores for External Controls | compute_influences |
| Estimate ATE using RCT Data Only | estimate_rct |
| Estimate ATE for a Selected Data Subset (with GLM support) | estimate_selected |
| Select Optimal Subset of External Controls based on MSE | find_optimal_k |
| Generate Simulation Data for RCT and External Controls | gen_demo_data |
| Predictions for rlearner_krls objects | predict.rlearner_krls |
| Predictions for rlearner_lm | predict.rlearner_lm |
| R-learner implemented via kernel ridge regression with a Gaussian kernel | rlearner_krls |
| R-learner implemented via Ordinary Least Squares (Linear Model) | rlearner_lm |
