This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
(i) Normalization and/or variance stabilization of the data,
(ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.

c(person("Jean-Eudes", "Dazard",
role = c("aut", "cre"),
email = "jean-eudes.dazard@case.edu"),
person("Hua", "Xu",
role = "ctb",
email = "huaxu77@gmail.com"),
person("Alberto", "Santana",
role = "ctb",
email = "ahs4@case.edu"))

Estimation and Testing for the Multivariate t-Distribution
Routines to perform estimation and inference under the multivariate t-distribution.
Currently, the following methodologies are implemented: multivariate mean and covariance
estimation, hypothesis testing about the mean, equicorrelation and homogeneity of variances,
the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate
generation. Some auxiliary functions are also provided.

Multivariate hypothesis tests and the confidence intervals. This package can be used for hypothesis tests which are The Hotelling T Square Tests (One-Sample, Two Independent Samples, Paired Samples), The One Way MANOVA, The Multivariate Shapiro-Wilk Test for Multivariate Normality Test, The Bartlett Test for One Sample Covariance Matrix, The Box-M Test and The Bartlett's Sphericity Test.

Multivariate hypothesis tests and the confidence intervals. This package can be used for hypothesis tests which are The Hotelling T Square Tests (One-Sample, Two Independent Samples, Paired Samples), The One Way MANOVA,The Multivariate Shapiro-Wilk Test for Multivariate Normality Test, The Bartlett Test for One Sample Covariance Matrix, The Box-M Test and The Bartlett's Sphericity Test. For this package, we have benefited the studies Rencher (2003), Nel and Merwe (1986) <DOI: 10.1080/03610928608829342>, Tatlidil (1996), James (1994) <DOI: 10.2307/2333003>, Tsagris (2014), Villasenor Alva and Estrada (2009) <DOI: 10.1080/03610920802474465>.