Create what we call Elemental Graphics for display of
    anova results. 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 anova methods. This package
    represents a modification of the original granova
    package; the key change is to use 'ggplot2', Hadley
    Wickham's package based on Grammar of Graphics concepts
    (due to Wilkinson). The main function is granovagg.1w()
    (a graphic for one way ANOVA); two other functions
    (granovagg.ds() and granovagg.contr()) are to construct
    graphics for dependent sample analyses and
    contrast-based analyses respectively. (The function
    granova.2w(), which entails dynamic displays of data, is
    not currently part of 'granovaGG'.) The 'granovaGG'
    functions are to display data for any number of groups,
    regardless of their sizes (however, very large data
    sets or numbers of groups can be problematic). For
    granovagg.1w() a specialized approach is used to
    construct data-based contrast vectors for which anova
    data are displayed. The result is that the graphics use
    a straight line to facilitate clear interpretations
    while being faithful to the standard effect test in
    anova. The graphic results are complementary to
    standard summary tables; indeed, numerical summary
    statistics are provided as side effects of the graphic
    constructions. granovagg.ds() and granovagg.contr() provide
    graphic displays and numerical outputs for a dependent
    sample and contrast-based analyses. The graphics based
    on these functions can be especially helpful for
    learning how the respective methods work to answer the
    basic question(s) that drive the analyses. This means
    they can be particularly helpful for students and
    non-statistician analysts. But these methods can be of
    assistance for work-a-day applications of many kinds,
    as they can help to identify outliers, clusters or
    patterns, as well as highlight the role of non-linear
    transformations of data. In the case of granovagg.1w()
    and granovagg.ds() several arguments are provided to
    facilitate flexibility in the construction of graphics
    that accommodate diverse features of data, according to
    their corresponding display requirements. See the help
    files for individual functions.