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. Software Depends: R (>= 2.13.0), statmod, snow Suggests: RColorBrewer Jean-Eudes Dazard, PhD. <jxd101@case.edu> Jean-Eudes Dazard, PhD. <jxd101@case.edu>, with contributions from Hua Xu, PhD. <hxx58@case.edu>, and Alberto H. Santana, MBA. <ahs4@case.edu>, and J. Sunil Rao, PhD. <JRao@med.miami.edu>. GPL (>= 3) 2011-12-15 application/tgz http://CRAN.R-project.org/package=MVR