Computes experimental designs for two-arm experiments with
covariates using multiple methods, including: (0) complete randomization
and randomization with forced-balance; (1) greedy optimization of a balance
objective function via pairwise switching; (2) numerical optimization via
'gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5)
exhaustive enumeration for small sample sizes; (6) binary pair matching
using 'nbpMatching'; (7) binary pair matching plus method (1) to further
optimize balance; (8) binary pair matching plus method (3) to further
optimize balance; (9) Hadamard designs; and (10) simultaneous multiple
kernels. For the greedy, rerandomization, and related methods, three
objective functions are supported: Mahalanobis distance, standardized sums
of absolute differences, and kernel distances via the 'kernlab' library.
This package is the result of a stream of research that can be found in
Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random
Designs with Greatly Improved Balance." Biometrika 106(3), 695-701