Functions, documentation and example data to help divide
    geographic space into discrete polygons (zones).
    The package supports new zoning systems that are documented in the
    accompanying paper,
    "ClockBoard: A zoning system for urban analysis",
    by Lovelace et al. (2022) <doi:10.5311/JOSIS.2022.24.172>.
    The functions are motivated by research into the merits of different zoning systems
    (Openshaw, 1977) <doi:10.1068/a090169>. A flexible ClockBoard zoning system is
    provided, which breaks-up space by concentric rings
    and radial lines emanating from a central point.
    By default, the diameter of the rings grow according to the triangular number sequence
    (Ross & Knott, 2019) <doi:10.1080/26375451.2019.1598687> with the first 4 doughnuts
    (or annuli) measuring 1, 3, 6, and 10 km wide.
    These annuli are subdivided into equal segments (12 by default), creating the
    visual impression of a dartboard. Zones are labelled according to
    distance to the centre and angular distance from North, creating a simple
    geographic zoning and labelling system useful for visualising geographic
    phenomena with a clearly demarcated central location such as cities.

Computes a zonohedron from real vector generators.  The package also computes zonogons (2D zonotopes) and zonosegs (1D zonotopes).  An elementary S3 class for matroids is included, which supports matroids with rank 3, 2, and 1.  Optimization methods are taken from Heckbert (1985) <https://www.cs.cmu.edu/~ph/zono.ps.gz>.

An S3 class with methods for totally ordered indexed
             observations. It is particularly aimed at irregular time series
             of numeric vectors/matrices and factors. zoo's key design goals
             are independence of a particular index/date/time class and
             consistency with ts and base R by providing methods to extend
             standard generics.

This tool provides functions to load, segment and classify zooplankton images. 
  The image processing algorithms and the machine learning classifiers in this package are (will be, since these have not been added yet) direct ports of 
  an early 'python' implementation that can be found at <https://github.com/arickGrootveld/ZooID>. The model weights and datasets (also not added yet) 
  that are a part of this package can also be found at Arick Grootveld, Eva R. Kozak, Carmen Franco-Gordo (2023) <doi:10.5281/zenodo.7979996>.