Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map.

Performs Geometrical Archetypal Analysis after creating Grid Archetypes 
    which are the Cartesian Product of all minimum, maximum variable values. Since the archetypes 
    are fixed now, we have the ability to compute the convex composition coefficients for all 
    our available data points much faster by using the half part of Principal Convex Hull Archetypal method. 
    Additionally we can decide to keep as archetypes the closer to the Grid Archetypes ones. Finally 
    the number of archetypes is always 2 to the power of the dimension of our data points 
    if we consider them as a vector space. 
    Cutler, A., Breiman, L. (1994)      <doi:10.1080/00401706.1994.10485840>.
    Morup, M., Hansen, LK. (2012) <doi:10.1016/j.neucom.2011.06.033>.
    Christopoulos, DT. (2024) <doi:10.13140/RG.2.2.14030.88642>.

Tools to easily visualize geographic data of Morocco.
    This package interacts with data available through the
    'geomarocdata' package, which is available in a 'drat' 
    repository.   The size of the 'geomarocdata' package is
    approximately 12 MB.

Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution.

Provides facilities to read, write and validate geographic metadata 
 defined with ISO TC211 / OGC ISO geographic information metadata standards, and 
 encoded using the ISO 19139 and ISO 19115-3 (XML) standard technical specifications. 
 This includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119 (service metadata) 
 and 19136 (GML). Other interoperable schemas from the OGC are progressively supported 
 as well, such as the Sensor Web Enablement (SWE) Common Data Model, the OGC GML 
 Coverage Implementation Schema (GMLCOV), or the OGC GML Referenceable Grid (GMLRGRID).

Tools for geometric morphometric analyses and multidimensional
    data. Implements methods for morphological disparity analysis using
    bootstrap and rarefaction,
    as reviewed in Foote (1997) <doi:10.1146/annurev.ecolsys.28.1.129>.
    Includes integration and modularity testing,
    following Fruciano et al. (2013) <doi:10.1371/journal.pone.0069376>,
    using Escoufier's RV coefficient as test statistic as well as
    two-block partial least squares -
    PLS, Rohlf and Corti (2000) <doi:10.1080/106351500750049806>.
    Also includes vector angle comparisons,
    orthogonal projection for data correction
    (Burnaby (1966) <doi:10.2307/2528217>; Fruciano (2016) <doi:10.1007/s00427-016-0537-4>),
    and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992)
    <doi:10.1207/s15327906mbr2704_2>).