Mean-Variance Regularization
Function for Plotting Summary Cluster Diagnostic Plots
Mean-Variance Regularization Package
Function to Display the NEWS File
Function for Mean-Variance Regularization and Variance Stabilization
Function for Computing Mean-Variance Regularized T-test Statistic and ...
Function for Plotting Summary Normalization Diagnostic Plots
Real Proteomics Dataset
Function for Plotting Summary Variance Stabilization Diagnostic Plots
Multi-Groups Synthetic Dataset
Function for Plotting Summary Target Moments Diagnostic Plots
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.