Package: CBPS
Version: 0.13
Date: 2016-12-27
Title: Covariate Balancing Propensity Score
Author: Christian Fong <christianfong@stanford.edu>, 
		Marc Ratkovic <ratkovic@princeton.edu>, 
		Chad Hazlett <chazlett@ucla.edu>, 
		Xiaolin Yang <xiaoliny@princeton.edu>, 
		Kosuke Imai <kimai@princeton.edu>
Maintainer: Christian Fong <christianfong@stanford.edu>
Depends: R (>= 2.14), MASS, MatchIt, nnet, numDeriv
Imports:
Description: Implements the covariate balancing propensity score (CBPS) proposed
    by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. 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 from Imai and Ratkovic
    (2015) <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-
    valued treatment variables, continuous-valued treatments from Fong, Hazlett,
    and Imai (2015) <http://imai.princeton.edu/research/files/CBGPS.pdf>, and the
    situation with multiple distinct binary treatments administered simultaneously.
    In the future it will be extended to other settings including the generalization
    of experimental and instrumental variable estimates. Recently add the optimal
    CBPS which chooses the optimal balancing function and results in doubly robust
    and efficient estimator for the treatment effect.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Packaged: 2016-12-29 19:42:21 UTC; Christian
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2016-12-30 00:13:52