person(given = "Riccardo", family = "Rastelli", role = c("aut", "cre"), email = "riccardo.rastelli@wu.ac.at")
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2016) "Optimal Bayesian estimators for latent variable cluster models" <arXiv:1607.02325>.

person(given = "Riccardo", family = "Rastelli", role = c("aut", "cre"), email = "riccardoras@gmail.com")
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <DOI:10.1007/s11222-017-9786-y>.

Adam Kapelner, David Azriel and Abba M. Krieger
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.

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.
R (>= 3.2.0), rJava (>= 0.9-6), GreedyExperimentalDesignJARs
(>= 1.0)

Computes experimental designs for a
two-arm experiment with covariates via a number of methods.
(0) complete randomization and randomization with forced-balance.
(1) Greedily optimizing a
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.
We return all iterations of the designs for use in a permutation test.
(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>)
a la Bertsimas and Kallus.
(3) rerandomization,
(4) Karp's method for one covariate,
(5) exhaustive enumeration to find the
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
(9) Hadamard designs
We also allow for three objective functions:
Mahalanobis distance,
Sum of absolute differences standardized and
Kernel distances via the 'kernlab' library.
Rcpp, checkmate, nbpMatching, survey, kernlab, graphics,
grDevices, stats