Estimate non-regularized Gaussian graphical models, Ising models, 
  and mixed graphical models. The current methods consist of multiple regression, 
  a non-parametric bootstrap  <doi:10.1080/00273171.2019.1575716>, and Fisher z 
  transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, 
  predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.

Mosaic plots in the 'ggplot2' framework. Mosaic
    plot functionality is provided in a single 'ggplot2' layer by calling
    the geom 'mosaic'.

Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.

Estimation of partial correlation matrix using ridge penalty 
    followed by thresholding and reestimation. Under multivariate Gaussian 
    assumption, the matrix constitutes an Gaussian graphical model (GGM).

Graph estimation in Gaussian Graphical Models, following the method
  developed by C. Giraud, S. Huet and N. Verzelen (2012) <doi:10.1515/1544-6115.1625>.
  The main functions return the adjacency matrix of an undirected graph
  estimated from a data matrix.

Create plots that combine a phylogeny and frequency dynamics.
    Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo".
    Inspired by similar plots in publications of the labs of RE Lenski and JE
    Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose
    code this package exploits).

It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.

Geometries to plot network objects with 'ggplot2'.

Use multiple fill and colour scales in 'ggplot2'.

Uses 'ggplot2' to create normally distributed
    violin plots with specified means and standard deviations. This
    function can be useful in showing hypothetically normal distributions
    and confidence intervals.