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), 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-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.

Version: 1.20.0
Depends: R (≥ 2.15.0), parallel, statmod
Published: 2013-11-13
Author: Jean-Eudes Dazard [aut, cre], Hua Xu [ctb], Alberto Santana [ctb]
Maintainer: Jean-Eudes Dazard <jxd101 at case.edu>
License: GPL (≥ 3) | file LICENSE
URL: http://www.r-project.org
NeedsCompilation: yes
Citation: MVR citation info
Materials: NEWS
CRAN checks: MVR results

Downloads:

Reference manual: MVR.pdf
Package source: MVR_1.20.0.tar.gz
OS X binary: MVR_1.20.0.tgz
Windows binary: MVR_1.20.0.zip
Old sources: MVR archive