A set of tools supporting more flexible heatmaps. The graphics is grid-like using
  the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions
  constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming
  and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set,
  e.g., clustered and ordered by a supervised clustering method.

Create interactive cluster 'heatmaps' that can be saved as a stand-
    alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
    available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to
    show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical
    method for visualizing high-dimensional data, in which a table of numbers
    are encoded as a grid of colored cells. The rows and columns of the matrix
    are ordered to highlight patterns and are often accompanied by 'dendrograms'.
    'Heatmaps' are used in many fields for visualizing observations, correlations,
    missing values patterns, and more. Interactive 'heatmaps' allow the inspection
    of specific value by hovering the mouse over a cell, as well as zooming into
    a region of the 'heatmap' by dragging a rectangle around the relevant area.
    This work is based on the 'ggplot2' and 'plotly.js' engine. It produces
    similar 'heatmaps' to 'heatmap.2' with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), the ability to zoom from
    the 'dendrogram' panes, and the placing of factor variables in the sides of the
    'heatmap'.

The different methods for defining, detecting, and categorising the extreme events 
    known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016) <doi: 10.1016/j.pocean.2015.12.014> 
    and Hobday et al. (2018) <https://www.jstor.org/stable/26542662>. The functions in this package work on both air 
    and water temperature data. These detection algorithms may be used on non-temperature data as well.