c(person(given = "Hyun-Hwan", family = "Jeong", role = c("aut", "cre"), email = "jeong.hyunhwan@gmail.com"))

Title: Compare Baseline Characteristics Between Groups
Description: Compare baseline characteristics between two groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.

Description: Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.

role = c("aut", "cre"),email = "simeonem@umich.edu"),
Description: Classification performed on Big Data. It uses concepts from compressive sensing, and implements ensemble predictor (i.e., 'SuperLearner') and knockoff filtering as the main machine learning and feature mining engines.
Imports: stats , utils , prettydoc , foreach , SuperLearner, parallel ,
Suggests: knitr, rmarkdown , FNN , e1071 , missForest , knockoff ,
caret , smotefamily , xgboost , bartMachine , glmnet ,

Title: R Package for Covariate Balancing Propensity Score
Author: Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai
Depends: R (>= 2.14), MASS, arm, MatchIt
Description: CBPS is an R package that implements the covariate
balancing propensity score proposed by Imai and Ratkovic
(2012).  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.
Currently, the package can only deal with binary treatment in
the cross section setting but in the future it will be extended
to other settings including non-binary treatments and causal

Title: Covariate Balancing Propensity Score
Author: Christian Fong <christianfong@stanford.edu>, Marc Ratkovic <ratkovic@princeton.edu>, Chad Hazlett <chazlett@ucla.edu>, Kosuke Imai <kimai@princeton.edu>
Depends: R (>= 2.14), MASS, MatchIt, nnet, numDeriv
Description: Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014; 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, 2014), treatments with three- and four-valued treatment variables, continuous-valued treatments (Fong, Hazlett, and Imai, 2015), 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.
Packaged: 2015-09-20 22:14:19 UTC; Christian