Multivariate matching in observational studies typically has two goals: 1. to construct treated and control groups that have similar distribution of observed covariates and 2. to produce matched pairs or sets that are homogeneous in a few priority variables. This packages implements a network-flow-based method built around a tripartite graph that can simultaneously achieve both goals. A detailed explanation of the workflow and numerous examples are given in the vignette.
| Version: | 1.1.0 |
| Imports: | ggplot2, mvnfast, rcbalance, Rcpp, stats, utils |
| LinkingTo: | Rcpp |
| Suggests: | dplyr, knitr, optmatch, RItools, rmarkdown |
| Published: | 2021-02-17 |
| Author: | Bo Zhang [aut, cre] |
| Maintainer: | Bo Zhang <bozhan at wharton.upenn.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| CRAN checks: | match2C results |
| Reference manual: | match2C.pdf |
| Vignettes: |
Tutorial for R Package match2C |
| Package source: | match2C_1.1.0.tar.gz |
| Windows binaries: | r-devel: match2C_1.1.0.zip, r-release: match2C_0.1.0.zip, r-oldrel: match2C_0.1.0.zip |
| macOS binaries: | r-release: match2C_1.1.0.tgz, r-oldrel: match2C_1.1.0.tgz |
| Old sources: | match2C archive |
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