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Use optimization to estimate weights that balance covariates for binary, multinomial, and continuous treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.
Version: | 0.2.5 |
Depends: | R (≥ 3.4.0) |
Imports: | osqp (≥ 0.6.0.2), Matrix (≥ 1.2-13), ggplot2 (≥ 3.0.0) |
Suggests: | cobalt (≥ 3.8.0), twang (≥ 1.5) |
Published: | 2019-09-16 |
DOI: | 10.32614/CRAN.package.optweight |
Author: | Noah Greifer [aut, cre] |
Maintainer: | Noah Greifer <noah.greifer at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL] |
NeedsCompilation: | no |
Materials: | README NEWS |
In views: | CausalInference |
CRAN checks: | optweight results |
Reference manual: | optweight.pdf |
Package source: | optweight_0.2.5.tar.gz |
Windows binaries: | r-devel: optweight_0.2.5.zip, r-release: optweight_0.2.5.zip, r-oldrel: optweight_0.2.5.zip |
macOS binaries: | r-release (arm64): optweight_0.2.5.tgz, r-oldrel (arm64): optweight_0.2.5.tgz, r-release (x86_64): optweight_0.2.5.tgz, r-oldrel (x86_64): optweight_0.2.5.tgz |
Old sources: | optweight archive |
Reverse suggests: | cobalt, jointVIP, WeightIt |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.