dplyr, knitr, mvtnorm, optmatch, RItools, rmarkdown

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. The 'optmatch'
package, which is useful for running many functionalities, can be downloaded at
<https://github.com/markmfredrickson/optmatch>.

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.
The package also implements a template matching algorithm using a variant of the tripartite graph
design. A brief description of the workflow and some examples are given in the vignette. A more elaborated
tutorial can be found at <https://www.researchgate.net/publication/359513837_Tutorial_for_R_Package_match2C>.
dplyr, knitr, mvtnorm, RItools, rmarkdown

Optimal Multilevel Matching using a Network Algorithm
Performs multilevel matches for data with cluster-level treatments and individual-level outcomes using a network optimization algorithm.  Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis.
rcbsubset, plyr, coin, weights, mvtnorm, MASS,Hmisc

c(
person("Luke", "Keele",, "luke.keele@gmail.com", c("aut")),
person("Luke", "Miratrix",, "luke_miratrix@gse.harvard.edu", c("aut")),
person("Sam", "Pimentel",, "spi@berkeley.edu", c("aut", "cre")),
person("Paul", "Rosenbaum",,, c("ctb"))
)
Performs multilevel matches for data with cluster-
level treatments and individual-level outcomes using a network
optimization algorithm.  Functions for checking balance at the
cluster and individual levels are also provided, as are methods
for permutation-inference-based outcome analysis.  Details in
Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>.  The optmatch
package, which is useful for running many of the provided
functions, may be downloaded from Github at
<https://github.com/markmfredrickson/optmatch> if not available on
CRAN.
rcbsubset (>= 1.1.4), plyr, coin, weights, mvtnorm, MASS,
Hmisc, sandwich
Luke Keele [aut],
Luke Miratrix [aut],
Sam Pimentel [aut, cre],
Paul Rosenbaum [ctb]