Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to  match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.

Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.

Entropy Reweighting to Create Balanced Samples
Package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2008, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.

Robust Empirical Bayes Confidence Intervals
c(person(given = "Michal",
family = "Kolesár",
role = c("aut", "cre"),
email = "kolesarmi@googlemail.com",
comment = c(ORCID = "0000-0002-2482-7796")),
person(given= "Tim",
family = "Armstrong",
role = "ctb",
email = "timothy.armstrong@yale.edu"),
person(given = "Mikkel",
family = "Plagborg-Møller",
role = "ctb",
email = "mikkelpm@princeton.edu"))
Computes empirical Bayes confidence estimators and confidence
intervals in a normal means model. The intervals are robust in the sense
that they achieve correct coverage regardless of the distribution of the
means. If the means are treated as fixed, the intervals have an average
coverage guarantee. The implementation is based on Armstrong, Kolesár and
Plagborg-Møller (2020) <arXiv:2004.03448>.
spelling, testthat (>= 2.1.0), lpSolve, knitr, rmarkdown
Michal Kolesár [aut, cre] (<https://orcid.org/0000-0002-2482-7796>),
Tim Armstrong [ctb],
Mikkel Plagborg-Møller [ctb]