c(
person("Guido", "Imbens", email = "imbens@stanford.edu", role = c("aut")),
person("Stefan", "Wager", email = "swager@stanford.edu", role = c("aut", "cre")))
Optimized inference in regression discontinuity designs, as proposed by Imbens and Wager (2017) <arXiv:1705.01677>.
Guido Imbens [aut],
Stefan Wager [aut, cre]

c(person("Manuel", "Fontenla", role = c("aut", "cre"),
email = "manu.fontenla@gmail.com"))
Finds optimal trees in weighted graphs. In
particular, this package provides solving tools for minimum cost spanning
tree problems, minimum cost arborescence problems, shortest path tree
problems and minimum cut tree problem.

person("Katherine", "Brumberg", , "kbrum@wharton.upenn.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-5193-6250"))
Splits initial strata into refined strata that optimize covariate balance. For more information, please email the author for a copy of the accompanying manuscript. To solve the linear program, the 'Gurobi' commercial optimization software is recommended, but not required. The 'gurobi' R package can be installed following the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
R (>= 2.10), MASS, Rglpk, sampling, ggplot2
Katherine Brumberg [aut, cre] (<https://orcid.org/0000-0002-5193-6250>)

Targeted Stable Balancing Weights Using Optimization
Use optimization to estimate weights that balance covariates for binary, multinomial, continuous, and longitudinal treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate.

Use optimization to estimate weights that balance covariates for binary, multinomial, continuous, and longitudinal treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.