Provides a set of functions to interpret changes in
 compositional data based on a network representation of all pairwise ratio
 comparisons: computation of all pairwise ratio, construction of a
 p-value matrix of all pairwise tests of these ratios between
 conditions, conversion of this matrix to a network.

Provides a set of basic functions for creating Moodle XML
 output files suited for importing questions in Moodle (a learning
 management system, see <https://moodle.org/> for more information).

Analysis and plotting tools for snow profile data produced from manual snowpack 
  observations and physical snowpack models. The functions in this package support snowpack 
  and avalanche research by reading various formats of data (including CAAML, SMET,
  generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and 
  produce graphics such as stratigraphy and time series profiles. Package developed by 
  the Simon Fraser University Avalanche Research Program <http://www.avalancheresearch.ca>. 
  Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, 
  <doi:10.5194/nhess-20-1557-2020>).

Snow profiles describe the vertical (1D) stratigraphy of layered 
    snow with different layer characteristics, such as grain type, hardness, 
    deposition date, and many more. Hence, they represent a data format similar 
    to multivariate time series containing categorical, ordinal, and numerical 
    data types. Use this package to align snow profiles by matching their 
    individual layers based on Dynamic Time Warping (DTW). The aligned profiles 
    can then be assessed with an independent, global similarity measure that is 
    geared towards avalanche hazard assessment. Finally, through exploiting data
    aggregation and clustering methods, the similarity measure provides the
    foundation for grouping and summarizing snow profiles according to similar
    hazard conditions. In particular, this package allows for averaging large
    numbers of snow profiles with DTW Barycenter Averaging and thereby 
    facilitates the computation of individual layer distributions and summary 
    statistics that are relevant for avalanche forecasting purposes. 
    For more background information refer to Herla, Horton, Mair,
    and Haegeli (2021) <doi:10.5194/gmd-14-239-2021>, Herla, Mair, and Haegeli 
    (2022) <doi:10.5194/tc-16-3149-2022>, and Horton, Herla, and Haegeli (2024)
    <doi:10.5194/egusphere-2024-1609>.