Package: CBPS
Version: 0.6
Date: 2013-06-05
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, MatchIt, nnet, numDeriv
Imports:
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), treatments with three- and
four-valued treatment variables (Fong, 2013), and the situation
with multiple distinct binary treatments administered
simultaneously. 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: 2013-06-07 00:51:34 UTC; Christian
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-06-07 08:17:33

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), treatments with three- and four-valued treatment variables (Fong, 2013), and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including continuous treatments and the generalization of experimental and instrumental variable estimates.