hotellingT2_R

This function provides a base R implementation of Hotelling's T-squared
balance measure, renamed with `_R` for clarity that it is the R-based
analog to the JAX version in fastrerandomize.

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

hotellingT2_R function

<dl><dt>X</dt>
<dd>A numeric matrix of covariates (observations in rows).</dd>
<dt>W</dt>
<dd>A 0/1 treatment assignment vector of the same length as nrow(X).</dd></dl>

Arguments

Compute Hotelling's T-squared statistic in base R — hotellingT2_R

<dl>

<dt>X</dt>
<dd>A numeric matrix of covariates (observations in rows).</dd>


<dt>W</dt>
<dd>A 0/1 treatment assignment vector of the same length as nrow(X).</dd>

</dl>

hotellingT2_R: Compute Hotelling's T-squared statistic in base R

Description

Usage

Value

Arguments