Calculates the distance between each voter in a voter file
    (given lat/long coordinates or sf point geometries) and multiple
    polling or vote-by-mail drop box locations. Returns nearest location,
    k-nearest locations, or all locations within a distance threshold.
    Core computation uses the Haversine formula implemented in C++ via
    'Rcpp'.

Provides pre-compiled static 'VTK' libraries and headers so that
    downstream R packages can link against the Visualization Toolkit without
    requiring users to install 'VTK' manually. On all platforms the package
    first honours a user-supplied 'VTK_DIR' environment variable. On macOS it
    then tries 'Homebrew', followed by 'pkg-config'. On Linux it tries
    'pkg-config' and well-known system prefixes ('/usr', '/usr/local'). If no
    suitable system installation is found on macOS or Linux, pre-built static
    libraries are downloaded automatically from the package's GitHub releases.
    On Windows the package tries 'VTK_DIR', then 'Rtools45' 'pacman', then
    common 'MSYS2' prefixes, accepting both static ('.a') and shared
    ('.dll.a' import libs + DLLs) installations. When shared libraries are
    used, the VTK DLLs are staged in 'inst/vtk-dlls/' and an '.onLoad' hook
    prepends that directory to PATH via 'Sys.setenv()' when the package is
    loaded, and restored in '.onUnload()'. The pre-built fallback downloads
    static libraries by default; set 'VTK_LINK_TYPE=shared' before
    installation to download the DLL build instead. Note that on Windows the
    modules 'VTK_IONetCDF', 'VTK_IOHDF', 'VTK_GeovisCore', and
    'VTK_RenderingCore' are disabled because 'netcdf' and 'libproj' are not
    available in the 'Rtools45' 'static.posix' sysroot. Downstream packages
    can declare 'Imports: rvtk' and obtain the correct compiler and linker
    flags at install time via rvtk::CppFlags() and rvtk::LdFlagsFile().

Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described 
    in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes
    the total variance predicted in a regression model into weights that accurately reflect the proportional 
    contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios,
    RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.