Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.

Methods for analyzing (cell) motion in two or three dimensions.
	Available measures include displacement, confinement ratio, autocorrelation,
	straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks,
	steps, or subtracks with varying length. While the methodology has been developed for
	cell trajectory analysis, it is applicable to anything that moves including animals,
	people, or vehicles.
	Some of the methodology implemented in this packages was described by: 
	Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, 
	Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>,
	Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>,
	Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>,
	Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>,
	Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>,
  Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>,
  Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.

Implements a methodology for using cell volume
        distributions to estimate cell growth rates and division times
        that is described in the paper, "Cell Volume Distributions
        Reveal Cell Growth Rates and Division Times", by Michael
        Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona
        and Anne L. Plant, which appeared in the Journal of Theoretical
        Biology. In order to reproduce the analysis used to obtain
        Table 1 in the paper, execute the command
        "example(fitVolDist)".

Provides a 'Seurat'-compatible toolkit for marker gene identification,
    expression summarization, and visualization of annotated single-cell
    transcriptomic data. 'CellWindX' identifies top cell-type-enriched markers,
    calculates marker expression percentages and average expression values across
    cell groups, and generates publication-oriented dimensional reduction plots,
    marker heatmaps, and gene-level radar plots. The package includes built-in
    aesthetic palettes and supports both exploratory analysis and downstream figure
    preparation for single-cell atlas studies. The workflow is designed to
    complement single-cell analysis frameworks such as 'Seurat' described by
    Satija et al. (2015) <doi:10.1038/nbt.3192> and Hao et al. (2021)
    <doi:10.1016/j.cell.2021.04.048>, as well as heatmap visualization methods
    implemented in 'ComplexHeatmap' described by Gu et al. (2016)
    <doi:10.1093/bioinformatics/btw313>.