Greedy Experimental Design Construction
Tests if a vector has all elements the same
Implements forced balanced randomization
Implements complete randomization (without forced balance)
Computes a Euclidean-squared distance matrix rapidly
Gram Matrix Computation
Computes Objective Value From Allocation Vector
Computes Randomization Metrics (explained in paper) about a design alg...
Compute Binary Matching Strcuture
Create all binary Y's convenience function using a randomized design
Create PM designs
Computes varcov matrix for block designs
Generates homogeneous block design allocations rapidly
Generates a design matrix with standardized predictors.
Curate More Orthogonal Vectors Greedily
Curate More Orthogonal Vectors Greedily
Greedy Experimental Design Search
Create a Hadamard Design
Implements unequally allocated block designs
Implements unequally allocated complete randomization
Begin a Binary Match Search
Begin a Search for Binary Matching Followed by Greedy Switch Designs
Begin a Search for Binary Matching Followed by Rerandomization
Begin A Greedy Pair Switching Search
Begin A Greedy Pair Multiple Kernel Switching Search
Begin Gurobi Optimized Search
Begin Karp Search
Begin a Search for the Optimal Solution
Begin a Rerandomization Search
Compute Optimal Number of Treatments/Controls
Plots the objective value by iteration
Plots an order statistic of the object value as a function of number o...
Plots a summary of a greedy search object object
Plots a summary of a greedy_multiple_kernel_experimental_design obje...
Prints a summary of a binary_match_structure object
Prints a summary of a binary_then_greedy_experimental_design object
Prints a summary of a `binary_then_rerandomization_experimental_design...
Prints a summary of a greedy_experimental_design_search object
Prints a summary of a greedy_multiple_kernel_experimental_design obj...
Prints a summary of a karp_experimental_design_search object
Prints a summary of a optimal_experimental_design_search object
Prints a summary of a pairwise_matching_experimental_design_search o...
Prints a summary of a rerandomization_experimental_design_search obj...
Binary Pair Match Search
Returns unique allocation vectors that are binary matched
Returns unique allocation vectors that are binary matched
Returns the results (thus far) of the greedy design search
Query the Gurobi Results
Returns the results (thus far) of the karp design search
Returns the results (thus far) of the greedy design search for multipl...
Returns the results (thus far) of the optimal design search
Returns the results (thus far) of the rerandomization design search
Computes a numerically stable inverse of a covariance matrix
Returns the amount of time elapsed
Shuffles a vector rapidly
Standardizes the columns of a data matrix.
Starts the parallelized greedy design search.
Stops the parallelized greedy design search.
Prints a summary of a binary_match_structure object
Prints a summary of a binary_then_greedy_experimental_design object
Prints a summary of a `binary_then_rerandomization_experimental_design...
Prints a summary of a greedy_experimental_design_search object
Prints a summary of a greedy_multiple_kernel_experimental_design obj...
Prints a summary of a karp_experimental_design_search object
Prints a summary of a optimal_experimental_design_search object
Prints a summary of a pairwise_matching_experimental_design_search o...
Prints a summary of a rerandomization_experimental_design_search obj...
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 <doi:10.1093/biomet/asz026>. Krieger, A. M., Azriel, D. A., and Kapelner, A. (2023). "Better experimental design by hybridizing binary matching with imbalance optimization." Canadian Journal of Statistics, 51(1), 275-292 <doi:10.1002/cjs.11685>.