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CBPS  

Covariate Balancing Propensity Score
View on CRAN: Click here


Download and install CBPS package within the R console
Install from CRAN:
install.packages("CBPS")

Install from Github:
library("remotes")
install_github("cran/CBPS")

Install by package version:
library("remotes")
install_version("CBPS", "0.23")



Attach the package and use:
library("CBPS")
Maintained by
Christian Fong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-07-19
Latest Update: 2022-01-18
Description:
Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) . 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 optimal CBPS from Fan et al. (in-press) , several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) , treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) , propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) , 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.
How to cite:
Christian Fong (2012). CBPS: Covariate Balancing Propensity Score. R package version 0.23, https://cran.r-project.org/web/packages/CBPS
Previous versions and publish date:
0.1 (2012-07-19 19:16), 0.2 (2012-09-29 08:14), 0.3 (2013-01-31 17:07), 0.4 (2013-04-03 18:19), 0.5 (2013-05-08 07:23), 0.6 (2013-06-07 08:17), 0.7 (2013-07-25 00:30), 0.8 (2014-03-08 06:59), 0.9 (2014-08-14 00:49), 0.10 (2015-09-21 11:19), 0.11 (2016-05-13 00:44), 0.12 (2016-11-01 23:26), 0.13 (2016-12-30 00:13), 0.14 (2017-10-02 20:54), 0.15 (2017-11-26 23:16), 0.16 (2017-12-19 21:58), 0.17 (2018-01-30 00:42), 0.18 (2018-03-10 17:12), 0.19 (2018-06-17 08:07), 0.20 (2019-03-22 10:53), 0.21 (2019-08-21 09:40), 0.22 (2021-03-29 11:22)
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