Labels are a common construct in statistical software providing a 
  human readable description of a variable. While variable names are succinct,
  quick to type, and follow a language's naming conventions, labels may 
  be more illustrative and may use plain text and spaces. R does not provide
  native support for labels. Some packages, however, have made this feature
  available.  Most notably, the 'Hmisc' package provides labelling methods
  for a number of different object. Due to design decisions, these methods
  are not all exported, and so are unavailable for use in package development.
  The 'labelVector' package supports labels for atomic vectors in a light-weight
  design that is suitable for use in other packages.

Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time.

Provides functions for normalizing standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" (Netta Mendelson Cohen, Omer Schwartzman, Ram Jaschek, Aviezer Lifshitz, Michael Hoichman, Ran Balicer, Liran I. Shlush, Gabi Barbash & Amos Tanay, <doi:10.1038/s41591-021-01468-6>). Allows users to easily obtain normalized values for standard lab results, and to visualize their distributions. See more at <https://tanaylab.weizmann.ac.il/labs/>.

Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.

Allows the user to map multilingual free-text of occupations to a broad range
    of standardized classifications. The package facilitates automatic occupation coding
    (see, e.g., Gweon et al. (2017) <doi:10.1515/jos-2017-0006> and Turrell et al. (2019)
    <doi:10.3386/w25837>), where the ISCO to ESCO mapping is exploited to extend the
    occupations hierarchy, Le Vrang et al. (2014) <doi:10.1109/mc.2014.283>. Document
    vectorization is performed using the multilingual ESCO corpus. A method based on the 
    nearest neighbor search is used to suggest the closest ISCO occupation.