diff_in_means_R

Simple difference in means in base R

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>.

Connor Jerzak

fastrerandomize

Hardware-Accelerated Rerandomization for Improved Balance

Fucheng Warren Zhu

Aniket Sachin Kamat

Rebecca Goldstein

diff_in_means_R function

<dl><dt>Y</dt>
<dd>Numeric outcome vector.</dd>
<dt>W</dt>
<dd>0/1 treatment assignment vector.</dd></dl>

Arguments

Simple difference in means in base R — diff_in_means_R

<dl>

<dt>Y</dt>
<dd>Numeric outcome vector.</dd>


<dt>W</dt>
<dd>0/1 treatment assignment vector.</dd>

</dl>

diff_in_means_R: Simple difference in means in base R

Description

Usage

Value

Arguments