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

Implements a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data (Dazard and Rao, 2012). 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:

Normalization and/or variance stabilization of the data
Computation of mean-variance-regularized t-statistics (F-statistics to come)
Generation of diverse diagnostic plots
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

This branch (master) is the default one, that hosts the current development release (version 1.34.0).

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License

MVR 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 (>= 1.34.0) requires R-3.5.0 (2018-04-23). It was built and tested under R version 4.0.3 (2020-10-10) 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 of MVR, 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 (>= 1.34.0) of MVR from the GitHub repository:

install.packages("devtools")
library("devtools")
# devtools::install_github("jedazard/MVR")
install.packages("MVR", repos = c('https://jedazard.r-universe.dev', 'https://cloud.r-project.org'))

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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. jean-eudes.dazard@case.edu
Hua Xu, Ph.D. huaxu77@gmail.com
Alberto Santana, MBA. ahs4@case.edu

Maintainers:

Jean-Eudes Dazard, Ph.D. jean-eudes.dazard@case.edu

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).