Companion package to 'quallmer' providing an interactive 'shiny'
    application for manual coding, reviewing large language model (LLM)
    generated annotations, and computing inter-rater reliability metrics.
    Supports three modes: blind manual coding, LLM output validation, and
    agreement calculation. Computes standard reliability metrics including
    Krippendorff's alpha (Krippendorff 2019 <doi:10.4135/9781071878781>),
    Cohen's kappa, Fleiss' kappa (Fleiss 1971 <doi:10.1037/h0031619>),
    intraclass correlation coefficient (ICC), and percent agreement for
    nominal, ordinal, interval, and ratio data. Also computes gold-standard
    validation metrics including accuracy, precision, recall, and F1 scores
    following Sokolova and Lapalme (2009 <doi:10.1016/j.ipm.2009.03.002>).

Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and 
   converting it to a simple features object. These tools are focused on data that are drawn on a map
   that contains some type of polygon features. For each area identified on the map, the id numbers
   of these polygons can be entered as vectors and transformed using qualmap.

Automatic generation of maximally distinct qualitative color palettes,
    optionally tailored to color deficiency. A set of colors or a subspace of a
    color space is used as input and a final palette of specified size is
    generated by picking colors that maximize the minimum pairwise difference
    among the chosen colors. Adaptations to color vision deficiency,
    background colors, and white points are supported.

Provides functions to access survey results directly into R
    using the 'Qualtrics' API. 'Qualtrics'
    <https://www.qualtrics.com/about/> is an online survey and data
    collection software platform. See <https://api.qualtrics.com/> for
    more information about the 'Qualtrics' API.  This package is
    community-maintained and is not officially supported by 'Qualtrics'.

Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.