fastrerandomize: Hardware-Accelerated Rerandomization for Improved Balance
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>. 
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=fastrerandomize
to link to this page.