An interface to the 'Mangal' database - a collection of ecological networks. This package includes functions to work with the 'Mangal RESTful API' methods (<https://mangal-interactions.github.io/mangal-api/>).

Edit and simplify 'geojson', 'Spatial', and 'sf'
    objects.  This is wrapper around the 'mapshaper' 'JavaScript' library
    by Matthew Bloch <https://github.com/mbloch/mapshaper/> to perform
    topologically-aware polygon simplification, as well as other
    operations such as clipping, erasing, dissolving, and converting
    'multi-part' to 'single-part' geometries.

Provides an interface to 'Mapzen'-based APIs (including 
    geocode.earth, Nextzen, and NYC GeoSearch) for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://www.mapzen.com/documentation/> for more information. The original 
    Mapzen has gone out of business, but 'rmapzen' can be set up to work with 
    any provider who implements the Mapzen API.

A port of the C++ routine for applying the marching cubes algorithm written by 
    Thomas Lewiner et al. (2012) <doi:10.1080/10867651.2003.10487582> into an R package. 
    The package supplies the contour3d() function, which takes a 3-dimensional array of voxel 
    data and calculates the vertices, vertex normals, and faces for a 3d mesh representing 
    the contour(s) at a given level.

Three robust marginal integration procedures for additive models based on local 
             polynomial kernel smoothers. As a preliminary estimator of the multivariate 
             function for the marginal integration procedure, a first approach uses local 
             constant M-estimators, a second one uses local polynomials of order 1 over all the
             components of covariates, and the third one uses M-estimators based on local 
             polynomials but only in the direction of interest. For this last approach, 
             estimators of the derivatives of the additive functions can be obtained. All three
             procedures can compute predictions for points outside the training set if desired. 
             See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.