Provides a gradient descent algorithm to find a geodesic relationship between realvalued independent variables and a manifoldvalued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2dimensional shape space. Besides the standard leastsquares 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 nsphere is a ndimensional 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, ndimensional 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 2K4; preshapes are represented as complex Kvectors 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/s112630120591y>.
Package details 


Author  HaYoung Shin [aut, cre], HeeSeok Oh [aut] 
Maintainer  HaYoung Shin <hayoung.shin@gmail.com> 
License  GPL3 
Version  0.2.0 
URL  https://github.com/hayoungshin1/GeodRegr 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.