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RIIM  

Randomization-Based Inference Under Inexact Matching
View on CRAN: Click here


Download and install RIIM package within the R console
Install from CRAN:
install.packages("RIIM")

Install from Github:
library("remotes")
install_github("cran/RIIM")

Install by package version:
library("remotes")
install_version("RIIM", "2.0.0")



Attach the package and use:
library("RIIM")
Maintained by
Jianan Zhu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-03-12
Latest Update: 2025-03-12
Description:
Randomization-based inference for average treatment effects in potentially inexactly matched observational studies. It implements the inverse post-matching probability weighting framework proposed by the authors. The post-matching probability calculation follows the approach of Pimentel and Huang (2024) <doi:10.1093/jrsssb/qkae033>. The optimal full matching method is based on Hansen (2004) <doi:10.1198/106186006X137047>. The variance estimator extends the method proposed in Fogarty (2018) <doi:10.1111/rssb.12290> from the perfect randomization settings to the potentially inexact matching case. Comparisons are made with conventional methods, as described in Rosenbaum (2002) <doi:10.1007/978-1-4757-3692-2>, Fogarty (2018) <doi:10.1111/rssb.12290>, and Kang et al. (2016) <doi:10.1214/15-aoas894>.
How to cite:
Jianan Zhu (2025). RIIM: Randomization-Based Inference Under Inexact Matching. R package version 2.0.0, https://cran.r-project.org/web/packages/RIIM. Accessed 06 Jun. 2026.
Previous versions and publish date:
2.0.0 (2025-03-12 18:40)
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Complete documentation for RIIM
Functions, R codes and Examples using the RIIM R package
Full RIIM package functions and examples
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