ARHT: Adaptable Regularized Hotelling's T^2 Test for High-DimensionalData
Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by Li et al., (2016) <doi:10.48550/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.
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