Reveals how data quality silently degrades during geographic
    transformations while variable labels remain unchanged. Demonstrates
    that transformation error is agnostic to both the variable
    (population, income, etc.) and the tool ('R', 'Python', etc.). Provides a
    reproducible audit framework for quantifying the shift from observed
    to imputed data at each transformation hop.

Contains all the formulae of the growth and trace element uptake model described in the equally-named
    Geoscientific Model Development paper (de Winter, 2017, <doi:10.5194/gmd-2017-137>). The model takes as input a file with X- and Y-coordinates of digitized
    growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the
    cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in
    the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass
    and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping
    results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in
    the form of XLSX files.

Secure handling of API keys can be difficult. This package provides
    secure convenience functions for 
    entering / handling API keys and opening connections via inversion of control
    on those keys. Works seamlessly between production and developer environments.

Powerful graphical displays and statistical tools for structured problem solving and diagnosis. 
    The functions of the 'sherlock' package are especially useful for applying the process of elimination as a problem diagnosis technique.
    The 'sherlock' package was designed to seamlessly work with the 'tidyverse' set of packages and provides a collection of graphical displays 
    built on top of the 'ggplot' and 'plotly' packages, such as different kinds of small multiple plots as well as helper functions such as 
    adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file.
    References:
    David Hartshorne (2019, ISBN: 978-1-5272-5139-7).
    Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467).