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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
stats |
| Suggests: |
testthat |
| Published: |
2018-03-27 |
| Author: |
Haoran Li [aut, cre] |
| Maintainer: |
Haoran Li <hrli at ucdavis.edu> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
ARHT results |
Documentation:
Downloads:
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