Ordination comprises several multivariate exploratory and
    explanatory techniques with theoretical foundations in geometric data
    analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and
    applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for
    foundations.
    Greenacre (2010, ISBN:978-84-923846) shows how the most established of
    these, including principal components analysis, correspondence analysis,
    multidimensional scaling, factor analysis, and discriminant analysis,
    rely on eigen-decompositions or singular value decompositions of
    pre-processed numeric matrix data.
    These decompositions give rise to a set of shared coordinates along which
    the row and column elements can be measured. The overlay of their
    scatterplots on these axes, introduced by Gabriel (1971)
    <doi:10.1093/biomet/58.3.453>, is called a biplot.
    'ordr' provides inspection, extraction, manipulation, and visualization
    tools for several popular ordination classes supported by a set of recovery
    methods. It is inspired by and designed to integrate into 'Tidyverse'
    workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.

Provides an alternative to R's built-in functionality for handling
    regular expressions, based on the Onigmo library. Offers first-class
    compiled regex objects, partial matching and function-based substitutions,
    amongst other features.

The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) <doi:10.1122/1.3544591>).