Provides statistical transformations for plotting empirical 
    ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and 
    generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.

Provides functions for defining 
    mixed graphs containing three types of edges, directed, 
    undirected and bi-directed, with possibly multiple edges.
    These graphs are useful because they capture fundamental
    independence structures in multivariate distributions
    and in the induced distributions after marginalization 
    and conditioning.
    The package is especially concerned with Gaussian graphical
    models for
    (i) ML estimation for directed acyclic graphs, undirected and 
    bi-directed graphs and ancestral graph models
    (ii) testing several conditional independencies
    (iii) checking global identification of DAG Gaussian models
    with one latent variable
    (iv) testing Markov equivalences and generating Markov 
    equivalent graphs of specific types.

A collection of functions to visualize spatial data and models
    on top of static maps from various online sources (e.g Google Maps and Stamen
    Maps). It includes tools common to those tasks, including functions for
    geolocation and routing.

Helper to add insets based on geom_sf() from 'ggplot2'.
    This package gives you a drop-in replacement for geom_sf() that supports
    adding a zoomed inset map without having to create and embed a separate plot.

A quick and easy way of plotting the columns of two matrices or 
    data frames against each other using 'ggplot2'. Although 'ggmatplot' doesn't 
    provide the same flexibility as 'ggplot2', it can be used as a workaround for 
    having to wrangle wide format data into long format for plotting with
    'ggplot2'.

Tools for assessing and diagnosing convergence of
    Markov Chain Monte Carlo simulations, as well as for graphically display
    results from full MCMC analysis. The package also facilitates the graphical
    interpretation of models by providing flexible functions to plot the
    results against observed variables, and functions to work with
    hierarchical/multilevel batches of parameters
    (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).