EHRmuse
0.0.2.2Multi-Cohort Selection Bias Correction using IPW and AIPW Methods
Overview
Comprehensive toolkit for addressing selection bias in binary disease models across diverse non-probability samples, each with unique selection mechanisms. It utilizes Inverse Probability Weighting (IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce selection bias effectively in multiple non-probability cohorts by integrating data from either individual-level or summary-level external sources. The package also provides a variety of variance estimation techniques. Please refer to Kundu et al. doi:10.48550/arXiv.2412.00228.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE r-devel-linux-x86_64-fedora-gcc
- NOTE r-devel-windows-x86_64
- NOTE2026-07-1610 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Repository
Repository practices
2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/ritoban1/ehrmuse on 2026-07-19.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
3 releases. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.0.2.2Latest
- RR 4.5.0 released · 2025-04-11
- 0.0.2.12025-01-28 · diff ↗
- 0.0.2.02025-01-20
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-01-20
- Total releases
- 3 / 1 yrs
- License
- GPL (>= 2) OSI
- Download size
- 32 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet