rcbalance-package: Large, Sparse Optimal Matching with Refined Covariate Balance
In rcbalance: Large, Sparse Optimal Matching with Refined Covariate Balance
rcbalance-package R Documentation
Large, Sparse Optimal Matching with Refined Covariate Balance
Description
Tools for large, sparse optimal matching of treated units
and control units in observational studies. Provisions are
made for refined covariate balance constraints, which include
fine and near-fine balance as special cases. Matches are
optimal in the sense that they are computed as solutions to
network optimization problems rather than greedy algorithms.
See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879>
and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv
package, which provides an alternative solver for
the underlying network flow problems, carries an
academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
This package computes sparse matches that are optimal under a set of refined covariate balance constraints. These constraints, provided by the user, are a set of nested categorical variables of decreasing imporance which must be marginally balanced as closely as possible in the resulting treated and matched control populations. For more detail see the references.
The main function is rcbalance, which takes a distance/sparsity object containing information about matchability of the treated and control units and a list of fine balance variables and produces a match. The build.dist.struct function can be used to construct the distance/sparsity object from covariate information. The count.pairings function can be used to assess the sparsity of a proposed match. The other functions are largely for internal use and should not be needed by the large majority of users.
By default the package uses the R package rlemon to solve the minimum-cost network flow optimization problems by which matches are computed. Alternatively, users may specify that the rrelaxiv package
should be used instead. However, this package carries an academic license
and is not available on CRAN so users must install it themselves.
Author(s)
Samuel D. Pimentel
Maintainer: Samuel D. Pimentel <spi@berkeley.edu>
References
Pimentel, S.D., Kelz, R.R., Silber, J.H., and Rosenbaum, P.R. (2015) Large, sparse optimal matching with refined covariate balance in an observational study of the health outcomes produced by new surgeons, JASA 110 (510), 515-527.
Pimentel, S.D. (2016) Large, sparse optimal matching with R package rcbalance, Obs. Studies 2, 4-23.
rcbalance documentation built on March 26, 2022, 1:07 a.m.
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| rcbalance-package | R Documentation |
Large, Sparse Optimal Matching with Refined Covariate Balance
Description
Tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
This package computes sparse matches that are optimal under a set of refined covariate balance constraints. These constraints, provided by the user, are a set of nested categorical variables of decreasing imporance which must be marginally balanced as closely as possible in the resulting treated and matched control populations. For more detail see the references.
The main function is rcbalance, which takes a distance/sparsity object containing information about matchability of the treated and control units and a list of fine balance variables and produces a match. The build.dist.struct function can be used to construct the distance/sparsity object from covariate information. The count.pairings function can be used to assess the sparsity of a proposed match. The other functions are largely for internal use and should not be needed by the large majority of users.
By default the package uses the R package rlemon to solve the minimum-cost network flow optimization problems by which matches are computed. Alternatively, users may specify that the rrelaxiv package
should be used instead. However, this package carries an academic license
and is not available on CRAN so users must install it themselves.
Author(s)
Samuel D. Pimentel
Maintainer: Samuel D. Pimentel <spi@berkeley.edu>
References
Pimentel, S.D., Kelz, R.R., Silber, J.H., and Rosenbaum, P.R. (2015) Large, sparse optimal matching with refined covariate balance in an observational study of the health outcomes produced by new surgeons, JASA 110 (510), 515-527.
Pimentel, S.D. (2016) Large, sparse optimal matching with R package rcbalance, Obs. Studies 2, 4-23.
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