Computes Randomization Metrics (explained in paper) about a design algorithm

Usage

compute_randomization_metrics(designs)

Arguments

designs: A matrix where each column is one design.

Value

A list of resulting data: the probability estimates for each pair in the design of randomness where estmates close to ~0.5 represent random assignment, then the entropy metric the distance metric, the maximum eigenvalue of the allocation var-cov matrix (operator norm) and the squared Frobenius norm (the sum of the squared eigenvalues)

Author

Adam Kapelner

Examples

if (FALSE) { # \dontrun{
designs = matrix(c(1, 0, 1, 0, 0, 1, 0, 1), nrow = 4, ncol = 2)
compute_randomization_metrics(designs)
} # }