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.

A 'ggplot2' extension for visualizing China’s map, offering customizable projections, boundary styles, and buffer zones for thematic maps. 
    Suitable for spatial data analysis and enhancing map visualization with flexible styling options.

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>).

Enhance a 'mice' imputation workflow with visualizations for
    incomplete and/or imputed data. The plotting functions produce
    'ggplot' objects which may be easily manipulated or extended. Use
    'ggmice' to inspect missing data, develop imputation models, evaluate
    algorithmic convergence, or compare observed versus imputed data.

Fit penalized multivariable linear mixed models with a single 
    random effect to control for population structure in genetic association 
    studies. The goal is to simultaneously fit many genetic variants at the 
    same time, in order to select markers that are independently associated 
    with the response. Can also handle prior annotation information, 
    for example, rare variants, in the form of variable weights. For more 
    information, see the website below and the accompanying paper: 
    Bhatnagar et al., "Simultaneous SNP selection and adjustment for 
    population structure in high dimensional prediction models", 2020, 
    <DOI:10.1371/journal.pgen.1008766>.