MVR: Mean-Variance Regularization

MVR 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), such as in omics-type data, among which 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- and F-statistics, (iii) Generation of diverse diagnostic plots, (iv) Computationally efficiency implementation, using C++ interfacing, and an option for parallel computing to enjoy a fast and easy experience in the R environment.

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