Provides a framework of tools to summarise, visualise, and explore 
  longitudinal data. It builds upon the tidy time series data frames used in the
  'tsibble' package, and is designed to integrate within the 'tidyverse', and
  'tidyverts' (for time series) ecosystems. The methods implemented include 
  calculating features for understanding longitudinal data, including 
  calculating summary statistics such as quantiles, medians, and numeric ranges,
  sampling individual series, identifying individual series representative of a 
  group, and extending the facet system  in 'ggplot2' to facilitate exploration of samples of data. These methods are
  fully described in the paper "brolgar: An R package to Browse Over 
  Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, 
  Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.

Miscellaneous R functions, including functions related to
    graphics (mostly for base graphics), permutation tests, running
    mean/median, and general utilities.

Summarizes key information about statistical
    objects in tidy tibbles. This makes it easy to report results, create
    plots and consistently work with large numbers of models at once.
    Broom provides three verbs that each provide different types of
    information about a model. tidy() summarizes information about model
    components such as coefficients of a regression. glance() reports
    information about an entire model, such as goodness of fit measures
    like AIC and BIC. augment() adds information about individual
    observations to a dataset, such as fitted values or influence
    measures.

Provides suite of functions to work with regression model
    'broom::tidy()' tibbles.  The suite includes functions to group
    regression model terms by variable, insert reference and header rows
    for categorical variables, add variable labels, and more.

Convert fitted objects from various R mixed-model packages
    into tidy data frames along the lines of the 'broom' package.
    The package provides three
    S3 generics for each model: tidy(), which summarizes a model's statistical findings such as
    coefficients of a regression; augment(), which adds columns to the original
    data such as predictions, residuals and cluster assignments; and glance(), which
    provides a one-row summary of model-level statistics.