Individual gene expression patterns are encoded into a series of eigenvector patterns ('WGCNA' package). Using the framework of linear model-based differential expression comparisons ('limma' package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see: Greenham K, Sartor RC, Zorich S, Lou P, Mockler TC and McClung CR. eLife. 2020 Sep 30;9(4). <doi:10.7554/eLife.58993>.

Implements the algorithm described in Jun Li and Alicia T. Lamere, 
    "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes"  (Unpublished). 
    Detects not only genes that show different 
    average expressions ("differential expression", DE), but also genes that show different 
    diversities of expressions in different groups ("differentially dispersed", DD). DD genes 
    can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the 
    quasi-likelihood function, and thus has superior robustness against outliers and other noise.
    Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi.
    (2) Added a function for estimating the proportion of outliers in the data.
    (3) Modified the input parameter names for clarity, and modified the output format for the main function.

An implementation by Chen, Li, and Zhang (2022) <doi: 10.1093/bioadv/vbac041> of the Depth Importance in Precision Medicine (DIPM) method 
             in Chen and Zhang (2022) <doi:10.1093/biostatistics/kxaa021> and Chen and 
             Zhang (2020) <doi:10.1007/978-3-030-46161-4_16>. The DIPM method is a classification 
             tree that searches for subgroups with especially poor or strong performance in a given treatment group.

Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization.
    Yu and Rosenbaum (2019) <doi:10.1111/biom.13098>.