Title: Greedy Experimental Design Construction
Author: Adam Kapelner, David Azriel and Abba M. Krieger
Description: Computes experimental designs for a
two-arm experiment with covariates by greedily optimizing a
balance objective function. This optimization provides lower
variance for the treatment effect estimator (and higher power)
while preserving a design that is close to complete randomization.
We return all iterations of the designs for use in a permutation test.

Additional functionality includes using branch and bound optimization
(via Gurobi) and exhaustive enumeration.
Depends: R (>= 3.2.0), rJava (>= 0.9-6), GreedyExperimentalDesignJARs
Packaged: 2018-07-15 11:54:06 UTC; AdamLenovo

two-arm experiment with covariates via a number of methods.
(0) complete randomization and randomization with forced-balance.
balance objective function via pairwise switching. This optimization
provides lower variance for the treatment effect estimator (and higher
power) while preserving a design that is close to complete randomization.
(2) The second is via numerical optimization
(via 'gurobi' which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>)
optimal solution (only for small sample sizes)
(6) Binary pair matching using the 'nbpMatching' library
(7) Binary pair matching plus (1) to further optimize balance
(8) Binary pair matching plus (3) to further optimize balance
We also allow for three objective functions:
Sum of absolute differences standardized and
Kernel distances via the 'kernlab' library.
Imports: Rcpp, checkmate, nbpMatching, survey, kernlab, graphics,

person("Adam", "Kapelner", email = "kapelner@qc.cuny.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5985-6792")),
person("David", "Azriel", email = "davidazr@technion.ac.il", role = "aut"),
person("Abba", "Krieger", email = "krieger@wharton.upenn.edu", role = "aut")
two-arm experiment with covariates via a number of methods:
(0) complete randomization and randomization with forced-balance,
We return all iterations of the designs for use in a permutation test,
optimal solution (only for small sample sizes),
(6) Binary pair matching using the 'nbpMatching' library,
(7) Binary pair matching plus design number (1) to further optimize balance,
(8) Binary pair matching plus design number (3) to further optimize balance,
In (1-9) we allow for three objective functions:
Kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in
Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>,
Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance
Imports: Rcpp, checkmate, nbpMatching, survey, stringr, stringi,
Author: Adam Kapelner [aut, cre] (<https://orcid.org/0000-0001-5985-6792>),