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, 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. The k-sphere is a k-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (k+1)-dimensional space. 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> and Kim, H. J., Adluru, N., Collins, M. D., Chung, M. K., Bendin, B. B., Johnson, S. C., Davidson, R. J. and Singh, V. (2014) <doi:10.1109/CVPR.2014.352>.

Version: | 0.1.0 |

Depends: | R (≥ 4.0.0) |

Imports: | zipfR (≥ 0.6.66), stats (≥ 3.6.2) |

Published: | 2020-08-24 |

Author: | Ha-Young Shin [aut, cre], Hee-Seok Oh [aut] |

Maintainer: | Ha-Young Shin <hayoung.shin at gmail.com> |

License: | GPL-3 |

NeedsCompilation: | no |

CRAN checks: | GeodRegr results |

Reference manual: | GeodRegr.pdf |

Package source: | GeodRegr_0.1.0.tar.gz |

Windows binaries: | r-devel: GeodRegr_0.1.0.zip, r-release: GeodRegr_0.1.0.zip, r-oldrel: GeodRegr_0.1.0.zip |

macOS binaries: | r-release (arm64): GeodRegr_0.1.0.tgz, r-release (x86_64): GeodRegr_0.1.0.tgz, r-oldrel: GeodRegr_0.1.0.tgz |

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