Add callout arrows, highlight data points, and show percent
    change between rows on 'ggplot2' charts in one line of code.
    annotate_callout() points at a data row with an arrow and label.
    annotate_change() draws a color-coded arrow between two rows and
    labels the delta as percent change, absolute difference, or
    percentage points. Designed for business charts, quarterly reports,
    and dashboards. Built on top of the 'ggpp' package.

A 'ggplot2' extension that creates publication-quality forest
    and funnel plots from 'meta' package objects or tidy data frames. Provides
    custom 'ggproto' geometries for study-level confidence intervals with
    weight-proportional squares, summary effect diamonds, prediction
    intervals, null-effect reference lines, and funnel-plot contours. Supports
    subgroup analysis, back-transformation of summary measures, on-the-fly
    effect pooling, effect and weight table columns, and journal-specific
    layout presets (JAMA, BMJ, RevMan5).

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>.