MVR: Mean-Variance Regularization
This 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 those 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.
- Jean-Eudes Dazard [aut, cre], Hua Xu [ctb], Alberto Santana [ctb]
- Date of publication
- 2016-10-26 00:18:37
- Jean-Eudes Dazard <firstname.lastname@example.org>
- GPL (>= 3) | file LICENSE
- Function for Plotting Summary Cluster Diagnostic Plots
- Function for Mean-Variance Regularization and Variance...
- Function to Display the NEWS File
- Mean-Variance Regularization Package
- Function for Computing Mean-Variance Regularized T-test...
- Function for Plotting Summary Normalization Diagnostic Plots
- Real Proteomics Dataset
- Function for Plotting Summary Variance Stabilization...
- Multi-Groups Synthetic Dataset
- Function for Plotting Summary Target Moments Diagnostic Plots
Files in this package