This small collection of functions provides what we call elemental graphics for display of analysis of variance
    results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) 
    <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. 
    The term elemental derives from the fact that each function is aimed at construction of
    graphical displays that afford direct visualizations of data with respect to the fundamental 
    questions that drive the particular analysis of variance methods. These functions can be 
    particularly helpful for students and non-statistician analysts. But these methods should be
    quite generally helpful for work-a-day applications of all kinds, as they can help to identify
    outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.

A minimal set of routines to calculate the Grantham distance
    <doi:10.1126/science.185.4154.862>. The Grantham distance attempts to
    provide a proxy for the evolutionary distance between two amino acids
    based on three key chemical properties: composition, polarity and
    molecular volume. In turn, evolutionary distance is used as a proxy for
    the impact of missense mutations. The higher the distance, the more
    deleterious the substitution is expected to be.

Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for
   summarizing the consensus behavior of biological pathways in the form of a
   template, and for quantifying the extent to which individual samples deviate
   from the template. GRAPE templates are based only on the relative rankings
   of the genes within the pathway and can be used for classification of tissue
   types or disease subtypes. GRAPE can be used to represent gene-expression
   samples as vectors of pathway scores, where each pathway score indicates the
   departure from a given collection of reference samples. The resulting pathway-
   space representation can be used as the feature set for various applications,
   including survival analysis and drug-response prediction. 
   Users of GRAPE should use the following citation:
   Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize 
   tissue-specific functionality from gene expression profiles. 
   BMC Bioinformatics, 18:317 (June 2017).