Mean-Variance Regularization.

MVR

Mean-Variance Regularization: a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data

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Description

MVR (Mean-Variance Regularization) 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:

1. Normalization and/or variance stabilization of the data

2. Computation of mean-variance-regularized t-statistics (F-statistics to come)

3. Generation of diverse diagnostic plots

4. Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a fast and easy experience in the R environment

See also below the package news with the R command: MVR.news().

All the codes are in the R folder and a manual (MVR.pdf) details the end-user (and internal) functions. At this stage and for simplicity, there are only 2 end-user function, 4 end-user diagnostic and plotting functions and 2 end-user datasets (synthetic and real). See the "MVR-package" introduction section of the manual for more details and examples.

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Branches

• The default branch (master) hosts the current development release (version 1.33.0).

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License

PRIMsrc is open source / free software, licensed under the GNU General Public License version 3 (GPLv3), sponsored by the Free Software Foundation. To view a copy of this license, visit GNU Free Documentation License.

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Downloads

CRAN downloads since October 1, 2012, the month the RStudio CRAN mirror started publishing logs:

CRAN downloads in the last month:

CRAN downloads in the last week:

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Requirements

MVR (>= 1.33.0) requires R-3.0.2 (2013-09-25). It was built and tested under R version 3.5.1 (2018-07-02) and Travis CI.

Installation has been tested on Windows, Linux, OSX and Solaris platforms.

See Travis CI build result:

See CRAN checks: .

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Installation

• To install the stable version (1.33.0) of MVR from the CRAN repository, simply download and install the current version (1.33.0) from the CRAN repository:

install.packages("MVR")

• Alternatively, you can install the most up-to-date development version (>= 1.33.0) of MVR from the GitHub repository, simply run the following using devtools:

install.packages("devtools")
library("devtools")
devtools::install_github("jedazard/MVR")

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Usage

• To load the MVR library in an R session and start using it:

library("MVR")

• Check the package news with the R command:

MVR.news()

• Check on how to cite the package with the R command:

citation("MVR")

etc...

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Acknowledgments

Authors:

• Jean-Eudes Dazard, Ph.D. ([email protected])
• Hua Xu, Ph.D. ([email protected])
• Alberto Santana, MBA. ([email protected])

Maintainers:

• Jean-Eudes Dazard, Ph.D. ([email protected])

Funding/Provision/Help:

• This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.
• This project was partially funded by the National Institutes of Health NIH - National Cancer Institute (P30-CA043703).

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References

• Dazard J-E. and J. S. Rao. Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data. Comput. Statist. Data Anal. (2012), 56(7):2317-2333. (The Official Journal of the International Association for Statistical Computing).

• Dazard J-E., Hua Xu and J. S. Rao. R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization. In JSM Proceedings, Section for Statistical Programmers and Analysts. Miami Beach, FL, USA: American Statistical Association IMS - JSM, 3849-3863. JSM (2011).

• Dazard J-E. and J. S. Rao. Regularized Variance Estimation and Variance Stabilization of High-Dimensional Data. In JSM Proceedings, Section for High-Dimensional Data Analysis and Variable Selection. Vancouver, BC, Canada: American Statistical Association IMS - JSM, 5295-5309. JSM (2010).