Dependency-free, ultra fast calculation of geodesic
    distances.  Includes the reference nanometre-accuracy geodesic
    distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by
    the 'sf' package, as well as Haversine and Vincenty distances. Default
    distance measure is the "Mapbox cheap ruler" which is generally more
    accurate than Haversine or Vincenty for distances out to a few hundred
    kilometres, and is considerably faster. The main function accepts one
    or two inputs in almost any generic rectangular form, and returns
    either matrices of pairwise distances, or vectors of sequential
    distances.

Methods for calculating gradient surface metrics for
    continuous analysis of landscape features.

Draw geospatial objects by clicks on the map.
    This packages can help data analyst who want to check
    their own geospatial hypothesis but has no ready-made geospatial objects.

Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y>.