c(person("Xuwen", "Zhu", role = c("aut", "cre"),
email = "xzhu20@cba.ua.edu"),
person("Volodymyr", "Melnykov", role = "aut"),
person("Shuchismita", "Sarkar", role = "ctb"),
person("Michael", "Hutt", role = c("ctb", "cph")),
person("Stephen", "Moshier", role = c("ctb", "cph")),
person("Rouben", "Rostamian", role = c("ctb", "cph")),
person("Carl Edward", "Rasmussen", role = c("ctb", "cph")),
person("Dianne", "Cook", role = c("ctb", "cph")))

MatchIt: Nonparametric Preprocessing for Parametric Casual Inference
Daniel Ho <daniel.ho@yale.edu>,
Kosuke Imai <kimai@Princeton.Edu>,
Gary King <king@harvard.edu>,
Elizabeth Stuart <stuart@stat.harvard.edu>
MatchIt selects matched samples of the
the original treated and control groups with similar
covariate distributions -- can be used to match exactly
on covariates, to match on propensity scores, or perform
a variety of other matching procedures.

Daniel Ho <daniel.e.ho@gmail.com>,
Kosuke Imai <kimai@Princeton.Edu>,
Gary King <king@harvard.edu>,
Elizabeth Stuart <stuart@stat.harvard.edu>

R (>= 2.2), MASS, optmatch, Matching, WhatIf, nnet, rpart, mgcv

Daniel Ho <daniel.e.ho@gmail.com>,
Elizabeth Stuart <stuart@stat.harvard.edu>,
Kosuke Imai <kimai@Princeton.Edu>,
Gary King <king@harvard.edu>
MatchIt preprocesses data by selecting approximate matched samples
of the treated and control groups with similar covariate
distributions, drawing on a large variety of matching methods.
After preprocessing data with MatchIt, whatever standard parametric
technique one might have used without preprocessing can be used, but
the results will be far less model dependent.

cem, optmatch, Matching, WhatIf, nnet, rpart, mgcv

Daniel Ho <daniel.e.ho@gmail.com>, Elizabeth Stuart
<stuart@stat.harvard.edu>, Kosuke Imai <kimai@Princeton.Edu>,
Gary King <king@harvard.edu>
MatchIt preprocesses data by selecting approximate matched
samples of the treated and control groups with similar
covariate distributions, drawing on a large variety of matching
methods. After preprocessing data with MatchIt, whatever
standard parametric technique one might have used without
preprocessing can be used, but the results will be far less
model dependent.