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.
|Author||Jean-Eudes Dazard [aut, cre], Hua Xu [ctb], Alberto Santana [ctb]|
|Date of publication||2017-05-29 13:17:08 UTC|
|Maintainer||Jean-Eudes Dazard <firstname.lastname@example.org>|
|License||GPL (>= 3) | file LICENSE|
|Package repository||View on CRAN|
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