ARHT - Adaptable Regularized Hotelling's T^2 Test for High-Dimensional
Data
Perform the Adaptable Regularized Hotelling's T^2 test
(ARHT) proposed by Li et al., (2016) <arXiv:1609.08725>. Both
one-sample and two-sample mean test are available with various
probabilistic alternative prior models. It contains a function
to consistently estimate higher order moments of the population
covariance spectral distribution using the spectral of the
sample covariance matrix (Bai et al. (2010)
<doi:10.1111/j.1467-842X.2010.00590.x>). In addition, it
contains a function to sample from 3-variate chi-squared random
vectors approximately with a given correlation matrix when the
degrees of freedom are large.