Package: MVR
Type: Package
Title: Mean-Variance Regularization
Description: 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.
Version: 1.00.0
Date: 2011-07-26
Depends: R (>= 2.13.0), statmod, snow
Suggests: RColorBrewer
Enhances:
Author: 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>.
Maintainer: Jean-Eudes Dazard, PhD. <jxd101@case.edu>
URL: http://proteomics.case.edu/jean_eudes_dazard.aspx
Repository: CRAN
License: GPL (>= 3)
LazyLoad: yes
Packaged: 2011-07-26 18:38:21 UTC; jxd101
Date/Publication: 2011-07-26 20:23:40

Description: 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.
Author: 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>.