A function built on 'ggplot2' that visualizes pairwise 'BLAST' alignment results as chord diagrams, intuitively displaying homologous regions between query and subject sequences.

Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.

William S. Cleveland's book 'Visualizing Data' is a classic piece 
	of literature on Exploratory Data Analysis. Although it was written 
	several decades ago, its content is still relevant as it proposes several 
	tools which are useful to discover patterns and relationships among the data 
	under study, and also to assess the goodness of fit o a model.  This package 
	provides functions to produce the 'ggplot2' versions of the visualization tools 
	described in this book and is thought to be used in the context of courses on 
	Exploratory Data Analysis.

Add mean comparison annotations to a 'ggplot'. 
             This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. 
             Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. 
             For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).

Create correlation heatmaps with 'ggplot2' and customise them
    with flexible annotation and clustering. Symmetric heatmaps can use
    triangular or mixed layouts, removing redundant information or displaying
    complementary information in the two halves. There is also support for
    general heatmaps not displaying correlations.

The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for
    reordering the correlation matrix and displays the significance level
    on the plot. It also includes a function for computing a matrix of
    correlation p-values.