Both Box-Cox and Yeo-Johnson transform non-normal distribution into a normal distribution. However, Box-Cox requires all samples to be positive, while Yeo-Johnson has no restrictions.
To me, it seems that Yeo-Johnson is superior to Box-Cox. Is there any reason why I shouldn't always blindly use Yeo-Johnson over Box-cox ? (ex: back-transform, interpretability, computation efficiency...)