Implements a moment-targeting normality transformation based on the
    simultaneous optimization of Tukey g-h distribution parameters. The method 
    is designed to minimize both asymmetry (skewness) and excess peakedness 
    (kurtosis) in non-normal data by mapping it to a standard normal distribution
    Cebeci et al (2026) <doi:10.3390/sym18030458>. Optimization is performed 
    by minimizing an objective function derived from the Anderson-Darling 
    goodness-of-fit statistic with Stephens's correction factor, utilizing the
    L-BFGS-B algorithm for robust parameter estimation. This approach provides an 
    effective alternative to power transformations like Box-Cox and Yeo-Johnson, 
    particularly for data requiring precise tail-behavior adjustment.

Function library for the identification and separation of exponentially
    decaying signal components in continuous-wave optically stimulated luminescence measurements.
    A special emphasis is laid on luminescence dating with quartz, which is known for
    systematic errors due to signal components with unequal physical behaviour.
    Also, this package enables an easy to use signal decomposition of
    data sets imported and analysed with the R package 'Luminescence'.
    This includes the optional automatic creation of HTML reports. Further information and tutorials
    can be found at <https://luminescence.de>.

Interface to 'OpenStreetMap API' for fetching and saving data
    from/to the 'OpenStreetMap' database
    (<https://wiki.openstreetmap.org/wiki/API_v0.6>).

Classify Open Street Map (OSM) features into meaningful functional 
    or analytical categories. Designed for OSM PBF files, e.g. from <https://download.geofabrik.de/> 
    imported as spatial data frames. A classification consists of a list of categories that are related to 
    certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main 
    osm_classify() function returns a classification data table giving, for each feature, the primary and 
    alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. 
    The package also contains a classification of OSM features by economic function/significance, 
    following Krantz (2023) <https://www.ssrn.com/abstract=4537867>.