Zhongheng Zhang,
Sir Run Run Shaw hospital,
Zhejiang university school of medicine

c(person("Simeone", "Marino",
role = c("aut", "cre"),email = "simeonem@umich.edu"),
person("Ivo", "Dinov", role = "aut"))
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.
stats , utils , prettydoc , foreach , SuperLearner, parallel ,
doParallel
knitr, rmarkdown , FNN , e1071 , missForest , knockoff ,
caret , smotefamily , xgboost , bartMachine , glmnet ,
randomForest
Simeone Marino [aut, cre],
Ivo Dinov [aut]

R Package for Covariate Balancing Propensity Score
Marc Ratkovic <ratkovic@princeton.edu>, Kosuke Imai
<kimai@princeton.edu>, Christian Fong <cjfong@princeton.edu>
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
inference with longitudinal data.

Christian Fong <christianfong@stanford.edu>, Marc Ratkovic <ratkovic@princeton.edu>, Chad Hazlett <chazlett@ucla.edu>, Kosuke Imai <kimai@princeton.edu>
R (>= 2.14), MASS, MatchIt, nnet, numDeriv
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.