person("Christian", "Fong", email = "cjfong@umich.edu", role = c("aut", "cre")),
person("Marc", "Ratkovic", email = "ratkovic@princeton.edu", role = c("aut")),
person("Kosuke", "Imai", email = "imai@Harvard.Edu", role = c("aut")),
person("Chad", "Hazlett", email = "chazlett@ucla.edu", role = c("ctb")),
person("Xiaolin", "Yang", email = "xiaoliny@princeton.edu", role = c("ctb")),
person("Sida", "Peng", email = "sp947@cornell.edu", role = c("ctb")),
person("Inbeom", "Lee", email = "il279@cornell.edu", role = c("ctb"))
covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation

balancing propensity score proposed by Imai and Ratkovic (2013;
JRSSB). The propensity score is estimated such that it
prediction of treatment assignment. The method, therefore,
The package also implements the generalization of CBPS to the
longitudinal settings so that it can be used in conjunction
with marginal structural models (Imai and Ratkovic, 2013a). In
the future it will be extended to other settings including
non-binary treatments and the generalization of experimental

Package: CBPS
Version: 0.5
Date: 2013-05-07
Title: R Package for Covariate Balancing Propensity Score
Author: Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai
<kimai@princeton.edu>, Christian Fong <cjfong@princeton.edu>
Maintainer: Marc Ratkovic <ratkovic@princeton.edu>
Depends: R (>= 2.14), MASS, arm, MatchIt, nnet
Imports: arm
Description: CBPS is an R package that implements the covariate
balancing propensity score proposed by Imai and Ratkovic (2013;
JRSSB). The propensity score is estimated such that it
maximizes the resulting covariate balance as well as the
prediction of treatment assignment. The method, therefore,
avoids an iteration between model fitting and balance checking.
The package also implements several extensions of the CBPS
beyond the cross-sectional, binary treatment setting.  The
current version implements the CBPS for longitudinal settings
so that it can be used in conjunction with marginal structural
models (Imai and Ratkovic, 2013a) as well as to three- and
four-valued treatment variables (Fong, 2013). In the future it
will be extended to other settings including continuous
treatments and the generalization of experimental and
instrumental variable estimates.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Packaged: 2013-05-08 00:45:49 UTC; ratkovic
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-08 07:23:17