The R Journal: article published in 2019, volume 11:1

Matching with Clustered Data: the CMatching Package in R
Massimo Cannas and Bruno Arpino , The R Journal (2019) 11:1, pages 7-21.

Abstract Matching is a well known technique to balance covariates distribution between treated and control units in non-experimental studies. In many fields, clustered data are a very common occurrence in the analysis of observational data and the clustering can add potentially interesting information. Matching algorithms should be adapted to properly exploit the hierarchical structure. In this article we present the CMatching package implementing matching algorithms for clustered data. The package provides functions for obtaining a matched dataset along with estimates of most common parameters of interest and model-based standard errors. A propensity score matching analysis, relating math proficiency with homework completion for students belonging to different schools (based on the NELS-88 data), illustrates in detail the use of the algorithms.

Received: 2018-10-26; online 2019-08-15, supplementary material, (1.9 KiB)
CRAN packages: CMatching, Matching, designmatch, optmatch, MatchIT, quickmatch, multiwayvcov
CRAN Task Views implied by cited CRAN packages: SocialSciences, Econometrics, ExperimentalDesign, HighPerformanceComputing, Optimization

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-018,
  author = {Massimo Cannas and Bruno Arpino},
  title = {{Matching with Clustered Data: the CMatching Package in R}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-018},
  url = {https://doi.org/10.32614/RJ-2019-018},
  pages = {7--21},
  volume = {11},
  number = {1}
}