Hardware-Accelerated Rerandomization for Improved Balance
A function to build the environment for fastrerandomize. Builds a cond...
Check if 'Python' and 'JAX' are available
Constructor for fastrerandomize randomizations
Constructor for fastrerandomize randomization test objects
Generate Complete Randomizations with Optional Balance Constraints
Draws a random sample of acceptable randomizations from all possible c...
Generate randomizations for a rerandomization-based experimental desig...
Plot method for fastrerandomize_test objects
Plot method for fastrerandomize_test objects
Print method for fastrerandomize_randomizations objects
Print method for fastrerandomize_test objects
Print timestamped messages with optional quieting
Fast randomization test
Summary method for fastrerandomize_randomizations objects
Summary method for fastrerandomize_test objects
Provides hardware-accelerated tools for performing rerandomization and randomization testing in experimental research. Using a 'JAX' backend, the package enables exact rerandomization inference even for large experiments with hundreds of billions of possible randomizations. Key functionalities include generating pools of acceptable rerandomizations based on covariate balance, conducting exact randomization tests, and performing pre-analysis evaluations to determine optimal rerandomization acceptance thresholds. The package supports various hardware acceleration frameworks including 'CPU', 'CUDA', and 'METAL', making it versatile across accelerated computing environments. This allows researchers to efficiently implement stringent rerandomization designs and conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) <doi:10.48550/arXiv.2310.00861>.
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