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.